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 $B \to \pi \pi, \pi K$ and $KK$ aresystematically investigated with and without flavor SU(3) symmetry. Independent analyses on $\pi \pi$ and $\pi K$ modes both favor a large ratio between color-suppressed tree ($C$) and tree ($T)$ diagram, which suggests that they are more likely to originate from long distance effects. The sizes of QCD penguin diagrams extracted individually from $\pi\pi$, $\pi K$ and $KK$ 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 $\gamma$ in consistency with the Standard Model (SM), but a large electro-weak penguin (P_{\tmop{EW}}) relative to $T + C$ 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 $W$-exchange ($E$), annihilation($A$) and penguin-annihilation diagrams($P_A$) are investigated. The implications to the branching ratios and CP violations in $K K$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