t-SURFF: Fully Differential Two-Electron Photo-Emission Spectra

The time dependent surface flux (t-SURFF) method is extended to single and double ionization of two electron systems. Fully differential double emission spectra by strong pulses at extreme UV and infrared wave length are calculated using simulation volumes that only accommodate the effective range of the atomic binding potential and the quiver radius of free electrons in the external field. For a model system we find pronounced dependence of shake-up and non-sequential double ionization on phase and duration of the laser pulse. Extension to fully three-dimensional calculations is discussed

Classification of Quench Dynamical Behaviours in Spinor Condensates

Thermalization of isolated quantum systems is a long-standing fundamental problem where different mechanisms are proposed over time. We contribute to this discussion by classifying the diverse quench dynamical behaviours of spin-1 Bose-Einstein condensates, which includes well-defined quantum collapse and revivals, thermalization, and certain special cases. These special cases are either nonthermal equilibration with no revival but a collapse even though the system has finite degrees of freedom or no equilibration with no collapse and revival. Given that some integrable systems are already shown to demonstrate the weak form of eigenstate thermalization hypothesis (ETH), we determine the regions where ETH holds and fails in this integrable isolated quantum system. The reason behind both thermalizing and nonthermalizing behaviours in the same model under different initial conditions is linked to the discussion of `rare' nonthermal states existing in the spectrum. We also propose a method to predict the collapse and revival time scales and how they scale with the number of particles in the condensate. We use a sudden quench to drive the system to non-equilibrium and hence the theoretical predictions given in this paper can be probed in experiments.Comment: 14 pages, 16 figure

Performance Analysis of Iteratively Decoded Variable-Length Space-Time Coded Modulation

It is demonstrated that iteratively Decoded Variable Length Space Time Coded Modulation (VL-STCM-ID) schemes are capable of simultaneously providing both coding gain as well as multiplexing and diversity gain. The VL-STCM-ID arrangement is a jointly designed iteratively decoded scheme combining source coding, channel coding, modulation as well as spatial diversity/multiplexing. In this contribution, we analyse the iterative decoding convergence of the VL-STCM-ID scheme using symbol-based three-dimensional EXIT charts. The performance of the VL-STCM-ID scheme is shown to be about 14.6 dB better than that of the Fixed Length STCM (FL-STCM) benchmarker at a source symbol error ratio of 10?4, when communicating over uncorrelated Rayleigh fading channels. The performance of the VL-STCM-ID scheme when communicating over correlated Rayleigh fading channels using imperfect channel state information is also studied

On the origin of the Fermi arc phenomena in the underdoped cuprates: signature of KT-type superconducting transition

We study the effect of thermal phase fluctuation on the electron spectral function $A(k,\omega)$ in a d-wave superconductor with Monte Carlo simulation. The phase degree of freedom is modeled by a XY-type model with build-in d-wave character. We find a ridge-like structure emerges abruptly on the underlying Fermi surface in $A(k,\omega=0)$ above the KT-transition temperature of the XY model. Such a ridge-like structure, which shares the same characters with the Fermi arc observed in the pseudogap phase of the underdoped cuprates, is found to be caused by the vortex-like phase fluctuation of the XY model.Comment: 5 page

Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively