PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model

Nonlinear optical diode effect in a magnetic Weyl semimetal

Weyl semimetals have emerged as a promising quantum material system to discover novel electrical and optical phenomena, due to their combination of nontrivial quantum geometry and strong symmetry breaking. One crucial class of such novel transport phenomena is the diode effect, which is of great interest for both fundamental physics and modern technologies. In the electrical regime, giant electrical diode effect (the nonreciprocal transport) has been observed in Weyl systems. In the optical regime, novel optical diode effects have been theoretically considered but never probed experimentally. Here, we report the observation of the nonlinear optical diode effect (NODE) in the magnetic Weyl semimetal CeAlSi, where the magnetic state of CeAlSi introduces a pronounced directionality in the nonlinear optical second-harmonic generation (SHG). By physically reversing the beam path, we show that the measured SHG intensity can change by at least a factor of six between forward and backward propagation over a wide bandwidth exceeding 250 meV. Supported by density-functional theory calculations, we establish the linearly dispersive bands emerging from Weyl nodes as the origin of the extreme bandwidth. Intriguingly, the NODE directionality is directly controlled by the direction of magnetization. By utilizing the electronically conductive semimetallic nature of CeAlSi, we demonstrate current-induced magnetization switching and thus electrical control of the NODE in a mesoscopic spintronic device structure with current densities as small as 5 kA/cm$^2$. Our results advance ongoing research to identify novel nonlinear optical/transport phenomena in magnetic topological materials. The NODE also provides a way to measure the phase of nonlinear optical susceptibilities and further opens new pathways for the unidirectional manipulation of light such as electrically controlled optical isolators.Comment: 28 pages, 12 figure

Waterscapes for Promoting Mental Health in the General Population

The WHO estimates that, with the development of urbanization, 25% of the population is suffering from psychological and mental distress. Preliminary evidence has suggested that aquatic environments and riparian areas, i.e., waterscapes, can benefit psychological and mental wellbeing. The aim of this study was to identify the processes of waterscape psychological and mental health promotion through aliterature review. We propose a design framework of waterscapes for achieving psychological and mental health in the general population that often visits waterscapes, which has the function of therapeutic landscapes through values of accessibility, versatility, habitats, and biodiversity. According to theories, waterscapes can improve psychological and mental health to divert negative emotions through mitigation (e.g., reduced urban heat island), instoration (e.g., physical activity and state of nature connectedness), and restoration (e.g., reduced anxiety/attentional fatigue). By accessing water (e.g., streams, rivers, lakes, wetlands, and the coast) and riparian areas, people can get in close contact with nature and spend more time in activities (e.g., walking, exploring, talking, and relaxing). Waterscapes with healing effects can enhance psychological resilience to promote people’s psychological and mental health. Future research should focus on ensuring an adequate supply of waterscapes and promoting the efficiency of waterscape ecosystem services on mental health. Moreover, fora deep understanding of the complexity of nature–human health associations, it is necessary to explore more consistent evidence for therapeutic waterscapes considering the characteristics and functional mechanisms of waterscape quality, in terms of freshness, luminescence, rippling or fluidity, and cultural value, to benefit public health and biodiversity conservation

Prediction of Manufacturing Quality of Holes Based on a BP Neural Network

In order to improve the manufacturing quality of holes (Φ3–Φ8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back propagation (BP) neural network algorithm. Four parameters of feed rate, spindle speed, drilling diameter, and cushion plate were taken as the input layer parameters to study the manufacturing quality of holes in four stack types: CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP. Delamination and tearing defects often appear in the drilling process; thus, a certain degree of defects in CFRP was selected as the output parameter, in an effort to build a prediction model of drilling quality. After the neural network model of the optimized hole-making process of an 8–14–1 three-layer topology was corrected by 170 steps, the error was reduced to 0.00016882, the regression fitting was 0.99978, and the fitting error of training samples was 10−2~10−5. The prediction model of the number of defective holes provided basically similar results to the experimental data. This indicates that the prediction model based on a BP neural network has good prediction ability. Based on the prediction of parameters, verification tests were performed, and the number of defective holes in CFRP was reduced while the manufacturing quality of the holes was improved significantly; the qualified rate of manufactured holes reached 97%