Study of Peeling of Single Crystal Silicon by Intense Pulsed Ion Beam

The surface peeling process induced by intense pulsed ion beam (IPIB) irradiation was studied. Single crystal silicon specimens were treated by IPIB with accelerating voltage of 350 kV current density of 130 A/cm2. It is observed that under smaller numbers of IPIB shots, the surface may undergo obvious melting and evaporation..

Exploring Effective Knowledge Distillation for Tiny Object Detection

Abstract Detecting tiny objects is a long-standing and critical problem in object detection, with broad real-world applications such as autonomous driving, surveillance, and medical diagnosis. Recent studies for tiny object detection often cause extra computational costs during inference due to introducing feature maps with increased resolution or additional network modules. This scarifies the inference speed for better detection accuracy and may heavily limit their availability to real-world applications. Therefore, this paper turns to knowledge distillation to improve the representation learning of a small model regarding both superior detection accuracy and fast inference speed. The masked scale-aware feature distillation and local attention distillation are proposed to address the critical issues in the distillation of tiny objects. Experimental results on two tiny benchmarks indicate that our method can bring noticeable performance gains to different detectors while keeping their original inference speeds. Our method also shows competitive performance compared to state-of-the-art methods for tiny object detection. Our code is available at https://github.com/haotianll/TinyKD.Abstract Detecting tiny objects is a long-standing and critical problem in object detection, with broad real-world applications such as autonomous driving, surveillance, and medical diagnosis. Recent studies for tiny object detection often cause extra computational costs during inference due to introducing feature maps with increased resolution or additional network modules. This scarifies the inference speed for better detection accuracy and may heavily limit their availability to real-world applications. Therefore, this paper turns to knowledge distillation to improve the representation learning of a small model regarding both superior detection accuracy and fast inference speed. The masked scale-aware feature distillation and local attention distillation are proposed to address the critical issues in the distillation of tiny objects. Experimental results on two tiny benchmarks indicate that our method can bring noticeable performance gains to different detectors while keeping their original inference speeds. Our method also shows competitive performance compared to state-of-the-art methods for tiny object detection. Our code is available at https://github.com/haotianll/TinyKD

A hybrid quantum–classical neural network with deep residual learning

AbstractInspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum–classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN, we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.Abstract Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum–classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN, we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed

Effects of Xinwei granule on expression levels of cyclin D1 and its upstream genes in gastric intraepithelial neoplasia tissues

Purpose: To explore the effects of Xinwei granule (XWG) on low-grade gastric intraepithelial neoplasia (LGIN) and the underlying mechanisms. Methods: To establish LGIN model, Wistar rats were treated with N-methyl-N'-nitrosoguanidine for 3 months. LGIN model rats were randomly grouped into five groups (n = 15), viz, negative control (NC), normal saline (NS) group, Xinwei granule (XWG) group, Weifuchun tablet (WFCT) group, and vatacoenayme tablet (VT) group. Normal rats (n = 17) served as negative control. Histological evaluation of gastric mucosa was undertaken using hematoxylin and eosin staining. Quantitative realtime polymerase chain reaction (qRT-PCR), western blot, and immunohistochemical assays were performed to determine mRNA expressions, protein expression, and the distribution of cyclin D1, kruppel-like factor 4 (KLF4), and p21-WAF1-CIP1, respectively. Results: Compared with LGIN group, the body weight of the rats increased in XWG, WFCT, and VT groups. The pathological characteristics of LGIN group were alleviated by XWG, WFCT and VT treatments. The positive expression of cyclin D1 was enhanced in LGIN group, but reduced in XWG, WFCT and VT groups. The expression levels of KLF4 and p21-WAF1-CIP1, upstream regulators of cyclin D1 reduced in LGIN groups. However, administration of XWG, WFCT and VT strengthened the expressions of KLF4 and p21-WAF1-CIP1. More importantly, the protective effects of XWG against LGIN were superior to those of WFCT and VT. Conclusion: Xinwei granules alleviate LGIN in vivo by inhibiting cyclin D1 expression and enhancing KLF4 and p21-WAF1-CIP1 expression

Study of Peeling of Single Crystal Silicon by Intense Pulsed Ion Beam

The surface peeling process induced by intense pulsed ion beam (IPIB) irradiation was studied. Single crystal silicon specimens were treated by IPIB with accelerating voltage of 350 kV current density of 130 A/cm2. It is observed that under smaller numbers of IPIB shots, the surface may undergo obvious melting and evaporation..

Study on Ablation Products of Zinc by Intense Pulsed Ion Beam Irradiation

As a kind of flash heat source, intense pulse ion beam can be used for material surface modification. The ablation effect has important influence on interaction between IPIB and material. Therefore, the understanding of ablation mechanism is of great significance to IPIB application..

The impact of new digital infrastructure on green total factor productivity

As a new engine driving economic development, new digital infrastructure plays a significant role in enhancing green total factor productivity. Based on 2011–2020 panel data covering 30 Chinese provinces, this study empirically investigates the effects and mechanisms of new digital infrastructure on green total factor productivity. The results show that new digital infrastructure can significantly improve regional green total factor productivity, and this conclusion remains valid after a series of robustness tests and regressions of instrumental variables. Further mechanism research shows that new digital infrastructure indirectly promotes the growth of green total factor productivity by improving capital misallocation and driving technological innovation, while there is no mediating mechanism of labor misallocation. In addition, there is significant heterogeneity in the impact of new digital infrastructure on green total factor productivity. Especially during periods of high government attention, in the eastern regions, and in areas with higher levels of human capital, the positive incentive effect of new digital infrastructure is more significant. This study provides empirical evidence and policy references for promoting and amplifying the green growth effects of new digital infrastructure

Efficient model-based bioequivalence testing

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard the PK parameters AUC and Cmax are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency the formulations are deemed to be similar if the 90%- confidence interval for these ratios falls between 0:8 and 1:25. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of AUC and Cmax using non-linear mixed effects models. Here we propose another test than the TOST, called BOT, and evaluate it through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of 0:05, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable

Study of the intense pulsed electron beam energy spectrum from BIPPAB-450

Intense pulsed particle beams have been widely used and studied as an effective method for material surface modification in the past several decades. Beihang Intense Pulsed PArticle Beams 450 accelerator (BIPPAB-450) can produce Intense Pulsed Ion Beams (IPIB) and Electron Beams (IPEB) in two modes with different Magnetically Insulated Diodes (MID). For IPEB, the pulse duration, accelerating voltage, total beam current are 100ns, up to 450keV and 3kA, respectively..