Controlled synthesis of monodisperse gold nanorods with different aspect ratios in the presence of aromatic additives

This paper reports the synthesis of monodisperse gold nanorods (GNRs) via a simple seeded growth approach in the presence of different aromatic additives, such as 7-bromo-3-hydroxy-2-naphthoic acid (7-BrHNA), 3-hydroxy-2-naphthoic acid (HNA), 5-bromosalicylic acid (5-BrSA), salicylic acid (SA) or phenol (PhOH). Effects of the aromatic additives and hydrochloric acid (HCl) on the structure and optical properties of the synthesized GNRs were investigated. The longitudinal surface plasmon resonance (LSPR) peak wavelength of the resulting GNRs was found to be dependent on the aromatic additive in the following sequence: 5-BrSA (778 nm) > 7-BrHNA (706 nm) > SA (688 nm) > HNA (676 nm) > PhOH (638 nm) without addition of HCl, but this was changed to 7-BrHNA (920 nm) > SA (890 nm) > HNA (872 nm) > PhOH (858 nm) > 5-BrSA (816 nm) or 7-BrHNA (1005 nm) > PhOH (995 nm) > SA (990 nm) > HNA (980 nm) > 5-BrSA (815 nm) with the addition of HCl or HNO3 respectively. The LSPR peak wavelength was increased with the increasing concentration of 7-BrHNA without HCl addition, however, there was a maximum LSPR peak wavelength when HCl was added. Interestingly, the LSPR peak wavelength was also increased with amount of HCl added. The results presented here thus established a simple approach to synthesize monodisperse GNRs of different LSPR wavelength

Linear Quadratic Nonzero Sum Differential Games with Asymmetric Information

This paper studies a linear quadratic nonzero sum differential game problem with asymmetric information. Compared with the existing literature, a distinct feature is that the information available to players is asymmetric. Nash equilibrium points are obtained for several classes of asymmetric information by stochastic maximum principle and technique of completion square. The systems of some Riccati equations and forward-backward stochastic filtering equations are introduced and the existence and uniqueness of the solutions are proved. Finally, the unique Nash equilibrium point for each class of asymmetric information is represented in a feedback form of the optimal filtering of the state, through the solutions of the Riccati equations

Stylized Pairing for Robust Adversarial Defense

Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two phenomenons, we conjecture that the preference of DNNs on exploiting object textures for decisions is one of the most important reasons for the existence of adversarial examples. At present, most adversarial defense methods are directly related to adversarial perturbations. In this paper, we propose an adversarial defense method independent of adversarial perturbations, which utilizes a stylized pairing technique to encourage logits for a pair of images and the corresponding stylized image to be similar. With stylized pairing training, DNNs can better learn shape-biased representation. We have empirically evaluated the performance of our method through extensive experiments on CIFAR10, CIFAR100, and ImageNet datasets. Results show that the models with stylized pairing training can significantly improve their performance against adversarial examples

A probabilistic approach to quasilinear parabolic PDEs with obstacle and Neumann problems

In this paper, by a probabilistic approach we prove that there exists a unique viscosity solution to obstacle problems of quasilinear parabolic PDEs combined with Neumann boundary conditions and algebra equations. The existence and uniqueness for adapted solutions of fully coupled forward-backward stochastic differential equations with reflections play a crucial role. Compared with existing works, in our result the spatial variable of solutions of PDEs lives in a region without convexity constraints, the second order coefficient of PDEs depends on the gradient of the solution, and the required conditions for the coefficients are weaker

虛擬的震撼 = The impact of virtuality

Paper Presented by: 黃海榮: 信任的建構與網上拍賣 蕭德健: 不被承認的「虛擬真實」- 香港人玩遊戲機的文化研

Imperceptible Adversarial Attack via Invertible Neural Networks

Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples. Though visual imperceptibility is the desired property of adversarial examples, conventional adversarial attacks still generate traceable adversarial perturbations. In this paper, we introduce a novel Adversarial Attack via Invertible Neural Networks (AdvINN) method to produce robust and imperceptible adversarial examples. Specifically, AdvINN fully takes advantage of the information preservation property of Invertible Neural Networks and thereby generates adversarial examples by simultaneously adding class-specific semantic information of the target class and dropping discriminant information of the original class. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that the proposed AdvINN method can produce less imperceptible adversarial images than the state-of-the-art methods and AdvINN yields more robust adversarial examples with high confidence compared to other adversarial attacks. Code is available at https://github.com/jjhuangcs/AdvINN