基于元胞自动机的股票价格宏观变化的研究

为了研究股票价格在宏观上的变化趋势以及产生这些变化的主要因素.提出了一种新的基于元胞自动机的模型分析股票价格宏观变化,构建了新的股票价格变化规则以及新的股票交易量的变化模型,假设了两大类影响股票价格宏观变化趋势的肉素,同时也细化了股票交易量的函数.模拟了当影响因素都利好,影响因素都利空以及影响因素利好利空交替出现的情况下,股票价格在宏观上的变化情况.仿真结果在一定程度上模拟了股票价格的宏观变化情况和股票价格变化的波动壤集性,说明了假设的两大类因素的合理性以及演化模型的正确性.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

CCA-secure unidirectional proxy re-encryption in the adaptive corruption model without random oracles

Up to now, it is still an open question of how to construct a chosen-ciphertext secure unidirectional proxy re-encryption scheme in the adaptive corruption model. To address this problem, we propose a new unidirectional proxy re-encryption scheme, and prove its chosen-ciphertext security in the adaptive corruption model without random oracles. Compared with the best known unidirectional proxy re-encryption scheme proposed by Libert and Vergnaud in PKC’08, our scheme enjoys the advantages of both higher effi...     

Dual graph regularized NMF model for social event detection from Flickr data

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.     

Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Structural and Multidisciplinary Optimization - Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve...     

军用软件开发过程的质量管理体系研究

军用软件系统的开发在我军信息化建设中占据至关重要的地位。大型武器装备系统的军用软件开发,成本高、风险大、周期长,参与单位及人员众多,如何能够确保大型军用软件的质量,增强武器装备性能,成为亟待解决的课题。本文将质量管理体系引入军用软件开发过程之中,通过强化过程控制,来确保大型军用软件的开发质量,对军队信息化建设,特别是软件系统的开发起到指导和借鉴作用。     

计算机维修模拟训练系统的设计与实现

探索了一种在多媒体环境下进行计算机及外部设备维修教学、训练的新方法 ,介绍了计算机维修模拟训练系统的设计与实现 ,给出了主要的设计思想、总体结构及关键技术。     

日本女作家吉屋信子的上海表象:以现地报告《战祸的北支上海行》为主线

针对抗日战争时期日本媒体的战争责任的问题,文章以活跃于大正和昭和年间的日本大众女作家吉屋信子各时期文学作品中的上海表象为线索,以吉屋的现地报告《战祸的北支上海行》为关注的焦点,分析了吉屋文学世界里所凝视的上海表象是“虚伪错误”的,而她提倡的中日两国通力合作的“共同幻想”也只是空洞的现实认识的记录而已.随着此种幻想的不断扩张,日本大众对已有一定倾向性的“上海负面形象”的认识也得以进一步的扩大.