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排序方式: 共有1375条查询结果,搜索用时 15 毫秒
1.
Liu Wen-Wen Yang Zi-Feng Fei Shao-Ming 《International Journal of Theoretical Physics》2021,60(11-12):4177-4195
International Journal of Theoretical Physics - We investigate the monogamy relations related to the concurrence, the entanglement of formation, convex-roof extended negativity, Tsallis-q... 相似文献
2.
Geometriae Dedicata - Inspired by a recent work of Grove and Petersen (Alexandrov spaces with maximal radius, 2018), where the authors studied positively curved Alexandrov spaces with largest... 相似文献
3.
为了对生鲜农产品电商O2O水平做出评估,本文设计了生鲜电商与传统流通体系融合发展的成熟度模型,确定了成熟度模型五个层级的十七个关键过程域及其目标。通过实例,运用突变级数法和灰靶决策对超市生鲜农产品020的成熟度进行了评估,使其发现经营过程中的薄弱环节并改善,向成熟度更高一层级迈进。结合突变级数和灰靶决策的成熟度模型避免了赋权的主观性,既提高了生鲜农产品电商与传统流通体系融合发展成熟度模型评估的准确性,又不失可靠性、合理性以及科学性,具有重要的实用价值。 相似文献
4.
Yongtao LIU 《Frontiers of Mathematics in China》2021,16(3):783
By using the perpetual cutoff method, we prove two discrete versions of gradient estimates for bounded Laplacian on locally finite graphs with exception sets under the condition of (K,N). This generalizes a main result of F. Münch who considers the case of CD(K, ) curvature. Hence, we answer a question raised by Münch. For that purpose, we characterize some basic properties of radical form of the perpetual cutoff semigroup and give a weak commutation relation between bounded Laplacian and perpetual cutoff semigroup in our setting. 相似文献
5.
Zhang Tinggui Xi Ya Fei Shao-Ming 《International Journal of Theoretical Physics》2021,60(8):2909-2915
We study the Bell nonlocality of high dimensional quantum systems based on quantum entanglement. A quantitative relationship between the maximal expectation value B of Bell operators and the quantum entanglement concurrence C is obtained for even dimension pure states, with the upper and lower bounds of B governed by C.
相似文献6.
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field. 相似文献
7.
Xun Zhang Lanyan Yang Bin Zhang Ying Liu Dong Jiang Xiaohai Qin Mengmeng Hao 《Entropy (Basel, Switzerland)》2021,23(4)
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures. 相似文献
8.
9.
Jingyun Luo Dexian Yin Kejing Yu Dr. Hongfu Zhou Dr. Bianying Wen Dr. Xiangdong Wang 《Chemphyschem》2022,23(4):e202100778
In order to reduce the pollutants of environment and electromagnetic waves, environment friendly polymer foams with outstanding electromagnetic interference shielding are imminently required. In this paper, a kind of electromagnetic shielding, biodegradable nanocomposite foam was fabricated by blending poly (butylene succinate) (PBS) with carbon nanotubes (CNTs) followed by foaming with supercritical CO2. The crystallization temperature and melting temperature of PBS/CNTs nanocomposites with 4 wt % of CNTs increased remarkably by 6 °C and 3.1 °C compared with that of pure PBS and a double crystal melting peak of various PBS samples appeared in DSC curves. Increasing the CNT content from 0 to 4 wt % leads to an increase of approximately 3 orders of magnitude in storage modulus and nearly 9 orders of magnitude in enhancement of electrical properties. Furthermore, CNTs endowed PBS nanocomposite foam with adjustable electromagnetic interference (EMI) shielding property, giving a specific EMI shielding effectiveness of 28.5 dB cm3/g. This study provides a promising methodology for preparing biodegradable, lightweight PBS/CNTs foam with outstanding electromagnetic shielding properties. 相似文献
10.
Guo Jiayun Ma Dongge Sun Fulin Zhuang Guilin Wang Qi Al-Enizi Abdullah M. Nafady Ayman Ma Shengqian 《中国科学:化学(英文版)》2022,65(9):1704-1709
Science China Chemistry - The heterojunction constructed of covalent organic frameworks (COFs) with adjustable structure and other photocatalysts has great potential in the field of photocatalysis.... 相似文献