Vibration Induced Non-adiabatic Geometric Phase and Energy Uncertainty of Fermions in Graphene

We investigate geometric phase of fermion states under relative vibrations of two sublattices in graphene by solving time-dependent Sch\"{o}dinger equation using Floquet scheme. In a period of vibration the fermions acquire different geometric phases depending on their momenta. There are two regions in the momentum space: the adiabatic region where the geometric phase can be approximated by the Berry phase and the chaotic region where the geometric phase drastically fluctuates in changing parameters. The energy of fermions due to vibrations shows spikes in the chaotic region. The results suggest a possible dephasing mechanism which may cause classical-like transport properties in graphene.Comment: 9 pages, 5 figure

MODIS Cloud Optical Property Retrieval Uncertainties Derived from Pixel-Level VNIR/SWIR Radiometric Uncertainties

Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of optical thickness and effective particle radius for liquid water and ice phase clouds employ a well-known VNIR/ SWIR solar reflectance technique. For this type of algorithm, we evaluate the quantitative uncertainty in simultaneous retrievals of these two cloud parameters to pixel-level radiometric calibration estimates and other fundamental (and tractable) error sources

The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts

Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A (SIR-A) L-band mission in 1982, radar images have been exploited to map subsurface features beneath a sandy cover of extremely low loss and low bulk humidity in some hyper-arid regions such as from the Japanese Earth Resources Satellite 1 (JERS-1) and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR). Therefore, we hypothesise that a Digital Elevation Model (DEM) derived by InSAR in hyper-arid regions is likely to represent a subsurface elevation model, especially for lower frequency radar systems, such as the L-band system (1.25 GHz). In this paper, we compare the surface appearance of radar images (L-band and C-band) with that of optical images to demonstrate their different abilities to show subsurface features. Moreover, we present an application of L-band InSAR to measure penetration depths in the eastern Sahara Desert. We demonstrate how the retrieved L-band InSAR DEM appears to be of a consistently 1–2 m lower elevation than the C-band Shuttle Radar Topography Mission (SRTM) DEM over sandy covered areas, which indicates the occurrence of penetration and confirms previous studies

Entropy, Dynamics and Instantaneous Normal Modes in a Random Energy Model

It is shown that the fraction f of imaginary frequency instantaneous normal modes (INM) may be defined and calculated in a random energy model(REM) of liquids. The configurational entropy S and the averaged hopping rate among the states R are also obtained and related to f, with the results R~f and S=a+b*ln(f). The proportionality between R and f is the basis of existing INM theories of diffusion, so the REM further confirms their validity. A link to S opens new avenues for introducing INM into dynamical theories. Liquid 'states' are usually defined by assigning a configuration to the minimum to which it will drain, but the REM naturally treats saddle-barriers on the same footing as minima, which may be a better mapping of the continuum of configurations to discrete states. Requirements of a detailed REM description of liquids are discussed

Phonon Coherence and New Set of Sidebands in Phonon-Assisted Photoluminescence

We investigate excitonic polaron states comprising a local exciton and phonons in the longitudinal optical (LO) mode by solving the Schr\"{o}dinger equation. We derive an exact expression for the ground state (GS), which includes multi-phonon components with coefficients satisfying the Huang-Rhys factors. The recombination of GS and excited polaron states gives one set of sidebands in photoluminescence (PL): the multi-phonon components in the GS produce the Stokes lines and the zero-phonon components in the excited states produce the anti-Stokes lines. By introducing the mixing of the LO mode and environal phonon modes, the exciton will also couple with the latter, and the resultant polaron states result in another set of phonon sidebands. This set has a zero-phonon line higher and wider than that of the first set due to the tremendous number of the environal modes. The energy spacing between the zero-phonon lines of the first and second sets is proved to be the binding energy of the GS state. The common exciton origin of these two sets can be further verified by a characteristic Fano lineshape induced by the coherence in the mixing of the LO and the environal modes.Comment: 5 pages, 3 figures 1 figure (fig. 1) replaced 1 figure (fig. 2) remove

Heterogeneous feature space based task selection machine for unsupervised transfer learning

© 2015 IEEE. Transfer learning techniques try to transfer knowledge from previous tasks to a new target task with either fewer training data or less training than traditional machine learning techniques. Since transfer learning cares more about relatedness between tasks and their domains, it is useful for handling massive data, which are not labeled, to overcome distribution and feature space gaps, respectively. In this paper, we propose a new task selection algorithm in an unsupervised transfer learning domain, called as Task Selection Machine (TSM). It goes with a key technical problem, i.e., feature mapping for heterogeneous feature spaces. An extended feature method is applied to feature mapping algorithm. Also, TSM training algorithm, which is main contribution for this paper, relies on feature mapping. Meanwhile, the proposed TSM finally meets the unsupervised transfer learning requirements and solves the unsupervised multi-task transfer learning issues conversely