16,049 research outputs found
Formation of Hydrogenated Graphene Nanoripples by Strain Engineering and Directed Surface Self-assembly
We propose a new class of semiconducting graphene-based nanostructures:
hydrogenated graphene nanoripples (HGNRs), based on continuum-mechanics
analysis and first principles calculations. They are formed via a two-step
combinatorial approach: first by strain engineered pattern formation of
graphene nanoripples, followed by a curvature-directed self-assembly of H
adsorption. It offers a high level of control of the structure and morphology
of the HGNRs, and hence their band gaps which share common features with
graphene nanoribbons. A cycle of H adsorption/desorption at/from the same
surface locations completes a reversible metal-semiconductor-metal transition
with the same band gap.Comment: 11 pages, 5 figure
Charmless decays B->pipi, piK and KK in broken SU(3)symmetry
Charmless B decay modes and aresystematically
investigated with and without flavor SU(3) symmetry. Independent analyses on
and modes both favor a large ratio between color-suppressed
tree () and tree ( diagram, which suggests that they are more likely to
originate from long distance effects. The sizes of QCD penguin diagrams
extracted individually from , and modes are found to
follow a pattern of SU(3) breaking in agreement with the naive factorization
estimates. Global fits to these modes are done under various scenarios of
SU(3)relations. The results show good determinations of weak phase in
consistency with the Standard Model (SM), but a large electro-weak penguin
(P_{\tmop{EW}}) relative to with a large relative strong phase are
favored, which requires an big enhancement of color suppressed electro-weak
penguin (P_{\tmop{EW}}^C) compatible in size but destructively interfering
with P_{\tmop{EW}} within the SM, or implies new physics. Possibility of
sizable contributions from nonfactorizable diagrams such as -exchange (),
annihilation() and penguin-annihilation diagrams() are investigated.
The implications to the branching ratios and CP violations in modes are
discussed.Comment: 27 pages, 9 figures, reference added, to appear in Phy.Rev.
Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States
Combining first-principles calculations and spin- and angle-resolved
photoemission spectroscopy measurements, we identify the helical spin textures
for three different Dirac cone states in the interfaced systems of a 2D
topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The
spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or
Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by
Rashba effect, and the hybridized Dirac cone between the former two states.
Further orbit- and atom-resolved analysis shows that s and pz orbits have a
clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of
upper (lower) Dirac cone, while px and py orbits have an additional radial spin
component. The Dirac cone states may reside on different atomic layers, but
have the same spin texture. Our results suggest that the unique spin texture of
Dirac cone states is a signature property of spin-orbit coupling, independent
of topology
Mu-synthesis PID control of full-car with parallel active link suspension under variable payload
This paper presents a combined μ -synthesis PID control scheme, employing a frequency separation paradigm, for a recently proposed novel active suspension, the Parallel Active Link Suspension (PALS). The developed μ -synthesis control scheme is superior to the conventional H∞ control, previously designed for the PALS, in terms of ride comfort and road holding (higher frequency dynamics), with important realistic uncertainties, such as in vehicle payload, taken into account. The developed PID control method is applied to guarantee good chassis attitude control capabilities and minimization of pitch and roll motions (low frequency dynamics). A multi-objective control method, which merges the aforementioned PID and μ -synthesis-based controls is further introduced to achieve simultaneously the low frequency mitigation of attitude motions and the high frequency vibration suppression of the vehicle. A seven-degree-of-freedom Sport Utility Vehicle (SUV) full car model with PALS, is employed in this work to test the synthesized controller by nonlinear simulations with different ISO-defined road events and variable vehicle payload. The results demonstrate the control scheme's significant robustness and performance, as compared to the conventional passive suspension as well as the actively controlled PALS by conventional H∞ control, achieved for a wide range of vehicle payload considered in the investigation
Towards Adversarial Robustness via Feature Matching
Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification. Adversarial training is one of the most effective defenses for improving the robustness of classifiers. We introduce an enhanced adversarial training approach in this work. Motivated by human's consistently accurate perception of surroundings, we explore the artificial attention of deep neural networks in the context of adversarial classification. We begin with an empirical analysis of how the attention of artificial systems will change as the model undergoes adversarial attacks. Observation is that the class-specific attention gets diverted and subsequently induces wrong prediction. To that end, we propose a regularizer encouraging the consistency in the artificial attention on the clean image and its adversarial counterpart. Our method shows improved empirical robustness over the state-of-the-art, secures 55.74% adversarial accuracy on CIFAR-10 with perturbation budget of 8/255 under the challenging untargeted attack in white-box settings. Further evaluations on CIFAR-100 also show our potential for a desirable boost in adversarial robustness for deep neural networks. Code and trained models of our work are available at: https://github.com/lizhuorong/Towards-Adversarial-Robustness-via-Feature-matching
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