Engineering Colloidal Lithography and Nanoskiving to Fabricate Rows of Opposing Crescent Nanogaps

A scalable fabrication route combining colloidal lithography and nanoskiving is reported for generating free-standing asymmetric metal nanostructures of crescent-shaped gold nanowires and rows of opposing crescents with and without nanogaps. Strong localized surface plasmon resonances and propagating surface plasmon polaritons are excited at the sharp tips of the crescent and in the sub-10 nm nanogaps. High-order resonance modes are excited due to the coupling between the resonances in the tips and gaps. The Raman signals are greatly enhanced due to the strong electric fields. In addition, the optical responses and electric field distributions can be controlled by the polarization of the incident light. The strong electric field enhancement coupled with facile, scalable fabrication make crescent-shaped nanostructures promising in nonlinear optics, optical trapping, and surface-enhanced spectroscopy

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition

Domain diversities, including inconsistent annotation and varied image collection conditions, inevitably exist among different facial expression recognition (FER) datasets, posing an evident challenge for adapting FER models trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning with adversarial learning mechanisms, ignoring the sibling facial action unit (AU) detection task, which has obtained great progress. Considering that AUs objectively determine facial expressions, this paper proposes an AU-guided unsupervised domain-adaptive FER (AdaFER) framework to relieve the annotation bias between different FER datasets. In AdaFER, we first leverage an advanced model for AU detection on both a source and a target domain. Then, we compare the AU results to perform AU-guided annotating, i.e., target faces that own the same AUs as source faces would inherit the labels from the source domain. Meanwhile, to achieve domain-invariant compact features, we utilize an AU-guided triplet training, which randomly collects anchor–positive–negative triplets on both domains with AUs. We conduct extensive experiments on several popular benchmarks and show that AdaFER achieves state-of-the-art results on all these benchmarks

AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition

Domain diversities, including inconsistent annotation and varied image collection conditions, inevitably exist among different facial expression recognition (FER) datasets, posing an evident challenge for adapting FER models trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning with adversarial learning mechanisms, ignoring the sibling facial action unit (AU) detection task, which has obtained great progress. Considering that AUs objectively determine facial expressions, this paper proposes an AU-guided unsupervised domain-adaptive FER (AdaFER) framework to relieve the annotation bias between different FER datasets. In AdaFER, we first leverage an advanced model for AU detection on both a source and a target domain. Then, we compare the AU results to perform AU-guided annotating, i.e., target faces that own the same AUs as source faces would inherit the labels from the source domain. Meanwhile, to achieve domain-invariant compact features, we utilize an AU-guided triplet training, which randomly collects anchor–positive–negative triplets on both domains with AUs. We conduct extensive experiments on several popular benchmarks and show that AdaFER achieves state-of-the-art results on all these benchmarks

Iterative learning control for a class of discrete-time singular systems

Abstract This paper is concerned with the iterative learning control problem for a class of discrete-time singular systems. According to the characteristics of the systems, a closed-loop PD-type learning algorithm is proposed and the convergence condition of the algorithm is established. It is shown that the algorithm can guarantee the system output converges to the desired trajectory on the whole time interval. Moreover, the presented algorithm is also suitable for discrete-time singular systems with state delay. Finally, the validity of the presented algorithm is verified by two numerical examples

Facile synthesis of fluorinated graphene for surface self-assembly of aluminum hydride

A facile method, refluxing with hydrothermal process, was used to synthesize the fluorinated graphene sheets (FGS) with high F/C ratio. Then, the surface of aluminum hydride (AlH3) was coated with FGS by means of liquid self-assembly process. The structure and performance of FGS were characterized through fourier transform infrared spectra (FTIR), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscope (TEM) and elemental analyzer. FTIR spectra show that the FGS have obvious absorption peak of CF bond. Raman spectra display many defects on FGS. XPS spectra and elemental analysis also prove the existence of CF bond, and the content of fluorine is more than 25%. From SEM photos we can see that FGS have thin layers with some curled wrinkles. TEM photos demonstrate intuitively the FGS has a few layers. At last, the aluminum hydride (AlH3) was coated with FGS by means of liquid self-assembly process, and AlH3 samples before and after treated were characterized through hydrogen content (H%) analysis, X-ray diffraction (XRD), FTIR, SEM and differential thermal analysis (DTA), ect. The results demonstrate that there is only a little FGS on the surface of AlH3, which won't affect the effective H% and release efficiency of hydrogen. AlH3@FGS also reduces the mechanical sensitivity of solid propellant with AlH3, which will remarkably promote the application of AlH3 in the novel high-energy solid propellant

Constructing Machine Tool Foundations Using an LMP Alloy

Currently, the construction of machine tool foundations is a complicated and lengthy procedure with a limited flexibility. In this paper, we present a novel system for constructing machine tool foundations that replaces the need for concrete or concrete-polymer hybrids with a low melting point (LMP) alloy. The system uses a hot bath method to maintain the LMP alloy grouting in liquid form. A fixing device is used to control the embedded depth and positional accuracy of the foundation bolt assembly. The grouting material is injected into the foundation pit by a filling device. This can be extracted from the foundation pit in a later stage with the aid of a recycling device, enabling new machine tool foundations to be manufactured by reusing the LMP alloy grouting material. A prototype was built to test the proposed design. The results show that the system can construct machine tool foundations in a single application, without the delays associated with concrete-based construction, lowering both the economic and environmental cost