Why Use a Hamilton Approach in QCD?

We discuss $QCD$ in the Hamiltonian frame work. We treat finite density $QCD$ in the strong coupling regime. We present a parton-model inspired regularisation scheme to treat the spectrum ($\theta$-angles) and distribution functions in $QED_{1+1}$. We suggest a Monte Carlo method to construct low-dimensionasl effective Hamiltonians. Finally, we discuss improvement in Hamiltonian $QCD$.Comment: Proceedings of Hadrons and Strings, invited talk given by H. Kr\"{o}ger; Text (LaTeX file), 3 Figures (ps file

Three-Body Recombination near a Narrow Feshbach Resonance in 6 Li

We experimentally measure and theoretically analyze the three-atom recombination rate, L3, around a narrow s-wave magnetic Feshbach resonance of 6Li−6Li at 543.3 G. By examining both the magnetic field dependence and, especially, the temperature dependence of L3 over a wide range of temperatures from a few μK to above 200 μK, we show that three-atom recombination through a narrow resonance follows a universal behavior determined by the long-range van der Waals potential and can be described by a set of rate equations in which three-body recombination proceeds via successive pairwise interactions. We expect the underlying physical picture to be applicable not only to narrow s wave resonances, but also to resonances in nonzero partial waves, and not only at ultracold temperatures, but also at much higher temperatures

Exchange routing information between new neighbor nodes to improve AODV performance

In Ad hoc On-Demand Distance Vector (AODV) protocol, once an on-demand link is established, it only maintains that link and does not care about any other paths. AODV may not use some more optimal or reserved paths which occur later but may improve its current transfer. We modify AODV that each node uses routing information provided by the new neighbour nodes to find out and update to better paths and create accumulated routes for later use. Our modeling results in NS2 show that the approach can create more optimal routes and significantly improve the performance with high mobility and traffic level network in term of delay and packet delivery ratio

Nonlinear Dynamics of Carbon Nanotubes Under Soft Alternating Current Actuation

This thesis work deals with electrostatically actuated Carbon Nanotubes (CNTs) cantilevers. Four forces are acting on the CNTs cantilever, namely damping, elastic, electrostatic and van der Waals forces. The van der Waals force is significant for values of 50 nm or lower of the gap between the CNTs and the ground substrate. As both electrostatic and van der Waals forces are nonlinear, and the CNTs electrostatic actuation is given by alternating current (AC) voltage, the CNTs undergo nonlinear parametric dynamics. The Method of Multiple Scales (MMS) and Reduced Order Model (ROM) are employed to investigate the system under soft excitation and weak nonlinearities. The frequency-amplitude and voltage-amplitude responses are reported in the cases of AC near half natural frequency and AC near primary natural frequency

Observation of a quenched moment of inertia in a rotating strongly interacting Fermi gas

We make a model-independent measurement of the moment of inertia of a rotating, expanding strongly-interacting Fermi gas. Quenching of the moment of inertia is observed for energies both below and above the superfluid transition. This shows that a strongly interacting Fermi gas with angular momentum can support irrotational flow in both the superfluid and collisional normal fluid regimes.Comment: 4 pages 5 figure

Parametric cooling of a degenerate Fermi gas in an optical trap

We demonstrate a novel technique for cooling a degenerate Fermi gas in a crossed-beam optical dipole trap, where high-energy atoms can be selectively removed from the trap by modulating the stiffness of the trapping potential with anharmonic trapping frequencies. We measure the dependence of the cooling effect on the frequency and amplitude of the parametric modulations. It is found that the large anharmonicity along the axial trapping potential allows to generate a degenerate Fermi gas with anisotropic energy distribution, in which the cloud energy in the axial direction can be reduced to the ground state value

Deep Clustering and Conventional Networks for Music Separation: Stronger Together

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as music source separation. Contrary to conventional networks that directly estimate the source signals, deep clustering generates an embedding for each time-frequency bin, and separates sources by clustering the bins in the embedding space. We show that deep clustering outperforms conventional networks on a singing voice separation task, in both matched and mismatched conditions, even though conventional networks have the advantage of end-to-end training for best signal approximation, presumably because its more flexible objective engenders better regularization. Since the strengths of deep clustering and conventional network architectures appear complementary, we explore combining them in a single hybrid network trained via an approach akin to multi-task learning. Remarkably, the combination significantly outperforms either of its components.Comment: Published in ICASSP 201