Entrapping multifunctional dendritic nanoparticles into a hydrogel for local therapeutic delivery and synergetic immunochemotherapy

Nano Research - Developing multifunctional nanoparticles to support new therapy models is a promising and challenging task to address the current dilemma on antitumor treatment. Herein, we...     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

舒曼钢琴音乐的双重性格特征

德国作曲家罗伯特·舒曼是十九世纪上半叶欧洲音乐文化史上最突出的人物之一。在他的钢琴音乐创作中,深刻地反映出浪漫主义音乐的特点。舒曼设计了一个想象中的“大卫同盟”,其中有两名核心盟员,一个叫弗洛列斯坦,另一个叫埃塞比乌斯。前者性格热情奔放,锋芒毕露;后者则文雅娴静,富于幻想,实际上这就是舒曼本人性格中的两个侧面。这种双重性格特征在舒曼的钢琴音乐中得到充分展示,并成为舒曼钢琴音乐的艺术特征之一。     

浅析艺术类专业《计算机基础》的教学方法

近年来,高校的计算机基础教学在不断发展,教学方法也在不断地改进。如何根据学生特点,使用各种行之有效的教学方法是提高教学质量的关键。文章结合教学实际,分析探讨了如何针对艺术类专业学生的特点,进行《计算机基础》教学方法改革。     

基于网络环境下的WEB开发基础课程的多媒体教学设计

随着Internet技术的高速发展,多媒体技术的应用已成为大部分教师的首选,网络教育、网络学习、基于网络的多媒体教学设计也成为授业者和学习者追求的目标。该文将以WEB开发基础科目为例,具体讲述现代多媒体教学模式的设计和网络资源的整合设计。     

Semi-supervised locally discriminant projection for classification and recognition

Semi-supervised dimensional reduction methods play an important role in pattern recognition, which are likely to be more suitable for plant leaf and palmprint classification, since labeling plant leaf and palmprint often requires expensive human labor, whereas unlabeled plant leaf and palmprint is far easier to obtain at very low cost. In this paper, we attempt to utilize the unlabeled data to aid plant leaf and palmprint classification task with the limited number of the labeled plant leaf or palmprint data, and propose a semi-supervised locally discriminant projection (SSLDP) algorithm for plant leaf and palmprint classification. By making use of both labeled and unlabeled data in learning a transformation for dimensionality reduction, the proposed method can overcome the small-sample-size (SSS) problem under the situation where labeled data are scant. In SSLDP, the labeled data points, combined with the unlabeled data ones, are used to construct the within-class and between-class weight matrices incorporating the neighborhood information of the data set. The experiments on plant leaf and palmprint databases demonstrate that SSLDP is effective and feasible for plant leaf and palmprint classification.     

语义驱动的关联规则挖掘算法研究

为了使传统的关联规则挖掘算法在结合到具体领域时具有更强的适应性,提出了DS-Apriori算法。该算法建立在语义本体的基础上,根据项集内部的语义相关度动态的确定该项集的最小支持度,并采用了项集语义相关度的增量计算方法。实验结果表明,DS-Apriori算法在很大程度上提高了关联规则挖掘算法的效率和效果。     

改进的证据理论在合作频谱感知中的应用

为了能够在认知无线电网络中有效、准确地检测出授权用户,提出了基于改进的证据理论的合作频谱感知方案设计方法.根据各个次用户对授权用户进行多次本地检测得出的时域DS融合结果,设计并改进融合顺序和融合结构,将多个时域DS融合结果送到融合中心进行空域DS融合,得到最终判决.在此基础上,给出了融合顺序、融合结构的改变对算法性能的...     

动态相位调整技术在FPGA中的设计与实现

提出一种基于FPGA的动态相位调整实现方案。在高速数据传输接口中,由于数据窗缩小以及传输路径不一致,造成数据和时钟信号在FPGA的接收端发生位偏移和字偏移。动态相位调整技术根据当前各数据线物理状态,对各信号线动态进行去偏移操作,克服了静态相位调整中参数不可再调的缺点,使接口不断适应外部环境的变化,从而保证数据的可靠传输。