535 research outputs found
Quantum Transport and Field Induced Insulating States in Bilayer Graphene pnp Junctions
We perform transport measurements in high quality bilayer graphene pnp
junctions with suspended top gates. At a magnetic field B=0, we demonstrate
band gap opening by an applied perpendicular electric field, with an On/Off
ratio up to 20,000 at 260mK. Within the band gap, the conductance decreases
exponentially by 3 orders of magnitude with increasing electric field, and can
be accounted for by variable range hopping with a gate-tunable density of
states, effective mass, and localization length. At large B, we observe quantum
Hall conductance with fractional values, which arise from equilibration of edge
states between differentially-doped regions, and the presence of an insulating
state at filling factor {\nu}=0. Our work underscores the importance of bilayer
graphene for both fundamental interest and technological applications.Comment: 4 figures, to appear in Nano Lett. Minor typos correcte
Case-Aware Adversarial Training
The neural network (NN) becomes one of the most heated type of models in
various signal processing applications. However, NNs are extremely vulnerable
to adversarial examples (AEs). To defend AEs, adversarial training (AT) is
believed to be the most effective method while due to the intensive
computation, AT is limited to be applied in most applications. In this paper,
to resolve the problem, we design a generic and efficient AT improvement
scheme, namely case-aware adversarial training (CAT). Specifically, the
intuition stems from the fact that a very limited part of informative samples
can contribute to most of model performance. Alternatively, if only the most
informative AEs are used in AT, we can lower the computation complexity of AT
significantly as maintaining the defense effect. To achieve this, CAT achieves
two breakthroughs. First, a method to estimate the information degree of
adversarial examples is proposed for AE filtering. Second, to further enrich
the information that the NN can obtain from AEs, CAT involves a weight
estimation and class-level balancing based sampling strategy to increase the
diversity of AT at each iteration. Extensive experiments show that CAT is
faster than vanilla AT by up to 3x while achieving competitive defense effect
Multiphysics Modeling of Thorium-Based Fuel Performance With Cr-Coated SiC/SiC Composite Under Normal and Accident Conditions
Using the finite element multiphysics modeling method, the performance of the thorium-based fuel with Cr-coated SiC/SiC composite cladding under both normal operating and accident conditions was investigated in this work. First, the material properties of SiC/SiC composite and chromium were reviewed. Then, the implemented model was simulated, and the results were compared with those of the FRAPTRAN code to verify the correctness of the model used in this work. Finally, the fuel performance of the Th0.923U0.077O2 fuel, Th0.923Pu0.077O2 fuel, and UO2 fuel combined with the Cr-coated SiC/SiC composite cladding and Zircaloy cladding, respectively, was investigated and compared under both normal operating and accident conditions. Compared with the UO2 fuel, the Th0.923U0.077O2 and Th0.923Pu0.077O2 fuels were found to increase the fuel centerline temperature under both normal operating and reactivity-initiated accident (RIA) conditions, but decrease the fuel centerline temperature under loss-of-coolant accident (LOCA) condition. Moreover, compared to the UO2 fuel with the Zircaloy cladding, thorium-based fuels with Cr-coated SiC/SiC composite cladding were found to show better mechanical performance such as delaying the failure time by about 3 s of the Cr-coated SiC/SiC composite cladding under LOCA condition, and reducing the plenum pressure by about 0.4 MPa at the peak value in the fuel rod and the hoop strain of the cladding by about 16% under RIA condition
Electrical Transport in High Quality Graphene pnp Junctions
We fabricate and investigate high quality graphene devices with contactless,
suspended top gates, and demonstrate formation of graphene pnp junctions with
tunable polarity and doping levels. The device resistance displays distinct
oscillations in the npn regime, arising from the Fabry-Perot interference of
holes between the two pn interfaces. At high magnetic fields, we observe
well-defined quantum Hall plateaus, which can be satisfactorily fit to
theoretical calculations based on the aspect ratio of the device.Comment: to appear in a special focus issue in New Journal of Physic
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