16,156 research outputs found
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)
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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
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