6,879 research outputs found
Optical responses in two-dimensional tilted semi-Dirac bands
Within linear response theory, the absorptive part of optical conductivities
are analytically calculated for distinct tilts in two-dimensional (2D) tilted
semi-Dirac bands (TSDBs). The transverse optical conductivities always vanish
. The
interband longitudinal optical conductivities (LOCs) in 2D TSDBs differ
qualitatively in the power-law scaling of as
and
. By
contrast, the intraband LOCs in 2D TSDBs depend on in the power-law
scaling and
. The
power-law scaling is similar to that in 2D untilted SDBs but distincts from a
uniform behavior independent of (or ) as
(or
) in 2D
tilted Dirac bands (TDBs). The universal power-law scaling further dictates
significant differences in the angular dependence of LOCs, which can be used to
characterize 2D TSDBs from 2D TDBs in the optical measurements. The
tilt-dependent behaviors of LOCs can qualitatively tell 2D TSDBs from 2D
untilted SDBs, but show similarities in the impact of band tilting between 2D
TSDBs and 2D TDBs.Comment: 16 pages, 3 figure
Highly Efficient Midinfrared On-Chip Electrical Generation of Graphene Plasmons by Inelastic Electron Tunneling Excitation
Inelastic electron tunneling provides a low-energy pathway for the excitation
of surface plasmons and light emission. We theoretically investigate tunnel
junctions based on metals and graphene. We show that graphene is potentially a
highly efficient material for tunneling excitation of plasmons because of its
narrow plasmon linewidths, strong emission, and large tunability in the
midinfrared wavelength regime. Compared to gold and silver, the enhancement can
be up to 10 times for similar wavelengths and up to 5 orders at their
respective plasmon operating wavelengths. Tunneling excitation of graphene
plasmons promises an efficient technology for on-chip electrical generation and
manipulation of plasmons for graphene-based optoelectronics and nanophotonic
integrated circuits.Comment: 12 pages, 7 figure
Anomalous acoustic plasmons in two-dimensional over-tilted Dirac bands
The over-tilting of type-II Dirac cones has led to various fascinating
quantum phenomena. Here we find two anomalous acoustic plasmons (AAPs) are
dictated by the distinct geometry of two-dimensional (2D) type-II Dirac cones,
far beyond the conventional \text{\ensuremath{\sqrt{q}}} plasmon. One AAP
originates from the strong hybridization of two pockets at one Dirac point,
whereas the other is attributed to the significant enhancement of the band
correlation around the open Fermi surface. Remarkably, the plasmons exhibit
valley-dependent chirality along the tilting direction due to the chiral
electron dispersion. Meanwhile, we discuss the tunability of plasmon dispersion
and lifetime by tuning the gap and dielectric substrate. Our work provides a
promising way to generate the novel plasmons in Dirac materials.Comment: 6 pages, 5 figure
Longitudinal optical conductivities in tilted Dirac bands
We report a unified theory based on linear response, for analyzing the
longitudinal optical conductivity (LOC) of materials with tilted Dirac cones.
Depending on the tilt parameter , the Dirac electrons have four phases,
untilted, type-I, type-II, and type-III; the Dirac dispersion can be isotropic
or anisotropic; the spatial dimension of the material can be one-, two-, or
three-dimensions. The interband LOCs and intraband LOCs in dimension (with
) are found to scale as to and
, respectively, where is the
frequency and the chemical potential. The interband LOCs always vanish in
one dimension due to lacking of extra spatial dimension. The angular dependence
of LOCs is found to characterize the band tilting, and the constant asymptotic
background values of LOC reflect features of the Dirac bands. The LOCs in the
anisotropic tilted Dirac cone can be connected to its isotropic counterpart by
a ratio that consists of Fermi velocities. The findings are valid for a great
many Dirac materials in the spatial dimensions of physical interests.Comment: 11 pages, 3 figure
Transport properties of graphene with one-dimensional charge defects
We study the effect of extended charge defects in electronic transport
properties of graphene. Extended defects are ubiquitous in chemically and
epitaxially grown graphene samples due to internal strains associated with the
lattice mismatch. We show that at low energies these defects interact quite
strongly with the 2D Dirac fermions and have an important effect in the
DC-conductivity of these materials.Comment: 6 pages, 5 figures. published version: one figure, appendix and
references adde
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
Recently using machine learning (ML) based techniques to optimize modern
database management systems has attracted intensive interest from both industry
and academia. With an objective to tune a specific component of a DBMS (e.g.,
index selection, knobs tuning), the ML-based tuning agents have shown to be
able to find better configurations than experienced database administrators.
However, one critical yet challenging question remains unexplored -- how to
make those ML-based tuning agents work collaboratively. Existing methods do not
consider the dependencies among the multiple agents, and the model used by each
agent only studies the effect of changing the configurations in a single
component. To tune different components for DBMS, a coordinating mechanism is
needed to make the multiple agents cognizant of each other. Also, we need to
decide how to allocate the limited tuning budget among the agents to maximize
the performance. Such a decision is difficult to make since the distribution of
the reward for each agent is unknown and non-stationary. In this paper, we
study the above question and present a unified coordinating framework to
efficiently utilize existing ML-based agents. First, we propose a message
propagation protocol that specifies the collaboration behaviors for agents and
encapsulates the global tuning messages in each agent's model. Second, we
combine Thompson Sampling, a well-studied reinforcement learning algorithm with
a memory buffer so that our framework can allocate budget judiciously in a
non-stationary environment. Our framework defines the interfaces adapted to a
broad class of ML-based tuning agents, yet simple enough for integration with
existing implementations and future extensions. We show that it can effectively
utilize different ML-based agents and find better configurations with 1.4~14.1X
speedups on the workload execution time compared with baselines.Comment: Accepted at 2023 International Conference on Management of Data
(SIGMOD '23
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