Stretching Micro Metal Particles into Uniformly Dispersed and Sized Nanoparticles in Polymer.

There is a longstanding challenge to disperse metal nanoparticles uniformly in bulk polymers for widespread applications. Conventional scale-down techniques often are only able to shrink larger elements (such as microparticles and microfibers) into micro/nano-elements (i.e. nanoparticles and nanofibers) without much altering their relative spatial and size distributions. Here we show an unusual phenomenon that tin (Sn) microparticles with both poor size distribution and spatial dispersion were stretched into uniformly dispersed and sized Sn nanoparticles in polyethersulfone (PES) through a stack and draw technique in thermal drawing. It is believed that the capillary instability plays a crucial role during thermal drawing. This novel, inexpensive, and scalable method overcomes the longstanding challenge to produce bulk polymer-metal nanocomposites (PMNCs) with a uniform dispersion of metallic nano-elements

Control of Fluid Dynamics by Nanoparticles in Laser Melting

Effective control of fluid dynamics is of remarkable scientific and practical significance. It is hypothesized that nanoparticles could offer a novel means to control fluid dynamics. In this study, laser melting was used to investigate the feasibility of tuning fluid dynamics by nanoparticles and possibly breaking existing limits of conventional laser processing techniques. Alumina nanoparticles reinforced nickel samples, fabricated through electrocodeposition, were used for laser melting experiments. Since the melt pool surface is controlled by the fluid dynamics, surface topographies were carefully studied to reveal the nanoparticle effect on the fluid dynamics. Characterizations of surface topographies and microstructures of pure Ni and Ni/Al2O3 nanocomposite were carried out before and after laser melting. The surface roughness of the Ni/Al2O3 nanocomposite sample was reduced significantly by laser melting, which broke the existing limit of laser surface polishing of pure Ni. It is believed that the nanoparticles increased the viscosity of the molten metal, thereby enhancing the viscous damping of the capillary oscillations in the melt pool, to produce a much smoother surface. Moreover, the experimental study also revealed that the viscosity enhancement by the nanoparticles effectively suppressed the thermocapillary flows which would introduce artificial asperities on a surface. The experimental results suggest that nanoparticles are effective in controlling melt pool dynamics and overcoming the existing limits of laser processing. The new methodology, fluid dynamics control by nanoparticles, opens a new pathway to enrich liquid based processes for broad applications

A distributed anomaly detection system for in-vehicle network using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall

Visual Content Privacy Protection: A Survey

Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Researchers have been working continuously to develop targeted privacy protection solutions, and there are several surveys to summarize them from certain perspectives. However, these surveys are either problem-driven, scenario-specific, or technology-specific, making it difficult for them to summarize the existing solutions in a macroscopic way. In this survey, a framework that encompasses various concerns and solutions for visual privacy is proposed, which allows for a macro understanding of privacy concerns from a comprehensive level. It is based on the fact that privacy concerns have corresponding adversaries, and divides privacy protection into three categories, based on computer vision (CV) adversary, based on human vision (HV) adversary, and based on CV \& HV adversary. For each category, we analyze the characteristics of the main approaches to privacy protection, and then systematically review representative solutions. Open challenges and future directions for visual privacy protection are also discussed.Comment: 24 pages, 13 figure

Observation of the chiral anomaly induced negative magneto-resistance in 3D Weyl semi-metal TaAs

Weyl semi-metal is the three dimensional analog of graphene. According to the quantum field theory, the appearance of Weyl points near the Fermi level will cause novel transport phenomena related to chiral anomaly. In the present paper, we report the first experimental evidence for the long-anticipated negative magneto-resistance generated by the chiral anomaly in a newly predicted time-reversal invariant Weyl semi-metal material TaAs. Clear Shubnikov de Haas oscillations (SdH) have been detected starting from very weak magnetic field. Analysis of the SdH peaks gives the Berry phase accumulated along the cyclotron orbits to be {\pi}, indicating the existence of Weyl points.Comment: Submitted in February'1

CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively.Comment: Accepted by IEEE Transactions on Image Processing 2022, 16 pages, 11 figure