9,307 research outputs found
Single-Particle Tunneling in Doped Graphene-Insulator-Graphene Junctions
The characteristics of tunnel junctions formed between n- and p-doped
graphene are investigated theoretically. The single-particle tunnel current
that flows between the two-dimensional electronic states of the graphene (2D-2D
tunneling) is evaluated. At a voltage bias such that the Dirac points of the
two electrodes are aligned, a large resonant current peak is produced. The
magnitude and width of this peak is computed, and its use for devices is
discussed. The influence of both rotational alignment of the graphene
electrodes and structural perfection of the graphene is discussed.Comment: 23 pages, 9 figures; added Section II(E) and associated figures, and
made other minor typographical correction
SymFET: A Proposed Symmetric Graphene Tunneling Field Effect Transistor
In this work, an analytical model to calculate the channel potential and
current-voltage characteristics in a Symmetric tunneling
Field-Effect-Transistor (SymFET) is presented. The current in a SymFET flows by
tunneling from an n-type graphene layer to a p-type graphene layer. A large
current peak occurs when the Dirac points are aligned at a particular drain-to-
source bias VDS . Our model shows that the current of the SymFET is very weakly
dependent on temperature. The resonant current peak is controlled by chemical
doping and applied gate bias. The on/off ratio increases with graphene
coherence length and doping. The symmetric resonant peak is a good candidate
for high-speed analog applications, and can enable digital logic similar to the
BiSFET. Our analytical model also offers the benefit of permitting simple
analysis of features such as the full-width-at-half-maximum (FWHM) of the
resonant peak and higher order harmonics of the nonlinear current. The SymFET
takes advantage of the perfect symmetry of the bandstructure of 2D graphene, a
feature that is not present in conventional semiconductors
Effective generation of Ising interaction and cluster states in coupled microcavities
We propose a scheme for realizing the Ising spin-spin interaction and atomic
cluster states utilizing trapped atoms in coupled microcavities. It is shown
that the atoms can interact with each other via the exchange of virtual photons
of the cavities. Through suitably tuning the parameters, an effective Ising
spin-spin interaction can be generated in this optical system, which is used to
produce the cluster states. This scheme does not need the preparation of
initial states of atoms and cavity modes, and is insensitive to cavity decay.Comment: 11pages, 2 figures, Revtex
Non-Abelian Quantum Hall Effect in Topological Flat Bands
Inspired by recent theoretical discovery of robust fractional topological
phases without a magnetic field, we search for the non-Abelian quantum Hall
effect (NA-QHE) in lattice models with topological flat bands (TFBs). Through
extensive numerical studies on the Haldane model with three-body hard-core
bosons loaded into a TFB, we find convincing numerical evidence of a stable
bosonic NA-QHE, with the characteristic three-fold quasi-degeneracy of
ground states on a torus, a quantized Chern number, and a robust spectrum gap.
Moreover, the spectrum for two-quasihole states also shows a finite energy gap,
with the number of states in the lower energy sector satisfying the same
counting rule as the Moore-Read Pfaffian state.Comment: 5 pages, 7 figure
Benchmarking Robustness of Text-Image Composed Retrieval
Text-image composed retrieval aims to retrieve the target image through the
composed query, which is specified in the form of an image plus some text that
describes desired modifications to the input image. It has recently attracted
attention due to its ability to leverage both information-rich images and
concise language to precisely express the requirements for target images.
However, the robustness of these approaches against real-world corruptions or
further text understanding has never been studied. In this paper, we perform
the first robustness study and establish three new diversified benchmarks for
systematic analysis of text-image composed retrieval against natural
corruptions in both vision and text and further probe textural understanding.
For natural corruption analysis, we introduce two new large-scale benchmark
datasets, CIRR-C and FashionIQ-C for testing in open domain and fashion domain
respectively, both of which apply 15 visual corruptions and 7 textural
corruptions. For textural understanding analysis, we introduce a new diagnostic
dataset CIRR-D by expanding the original raw data with synthetic data, which
contains modified text to better probe textual understanding ability including
numerical variation, attribute variation, object removal, background variation,
and fine-grained evaluation. The code and benchmark datasets are available at
https://github.com/SunTongtongtong/Benchmark-Robustness-Text-Image-Compose-Retrieval.Comment: Accepted by R0-FoMo: Workshop on Robustness of Few-shot and Zero-shot
Learning in Foundation Models at NeurIPS 202
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