题目：A Comprehensive Review of Cell-Type Deconvolution in Spatial Transcriptomic Data（空间转录组数据细胞类型解卷积综述）

作者：L. Wang, Y. Hu and L. Gao

来源：Big Data Mining and Analytics（大数据挖掘与分析）, vol. 9, no. 1, pp. 119-142.

摘要：Deciphering cell-type composition is critical for charting single-cell spatial maps and cellular atlases of organisms. Most Spatial Transcriptomics (ST) data lack single-cell resolution, computational deconvolution methods have emerged to characterize the composition and spatial heterogeneity of different cell types within spots and tissues. To date, various cell-type deconvolution methods have been developed, each exhibiting its own distinct advantages. To conduct a comprehensive review of these methods, we first provide a formal description of the deconvolution problem. Then, we analyze the advantages and pitfalls of these methods based on their mathematical models. We further discuss related downstream analyses, potential applications, and future directions. In summary, our review aims to guide researchers in gaining an in-depth understanding of the spatial deconvolution problem, enabling them to make informed choices in spatial analysis and advance research on related fields, such as the developmental biology, tumor microenvironment, disease pathology, and clinical treatments.

编者译：解析细胞类型组成对于绘制生物体的单细胞空间图谱和细胞图谱至关重要。由于大多数空间转录组学（ST）数据缺乏单细胞分辨率，计算解卷积方法应运而生，用于表征检测点内和组织中不同细胞类型的组成及空间异质性。迄今为止，研究人员已开发了多种细胞类型解卷积方法，各具优势。为了对这些方法进行全面综述，本文首先对解卷积问题进行了形式化描述。接着，基于其数学模型，本文分析了这些方法的优势与局限性，并进一步探讨了相关的下游分析、潜在应用及未来方向。综上所述，本综述旨在指导研究人员深入理解空间解卷积问题，使其能够在空间分析中做出明智的选择，并推动发育生物学、肿瘤微环境、疾病病理学及临床治疗等相关领域的研究。

题目：2025年中国高性能计算机发展现状分析与展望

作者：张云泉、袁良、袁国兴、李希代

来源：数据与计算发展前沿, 2025, 7(6): 92-100.

摘要：本文根据2025年11月发布的中国高性能计算机TOP100排行榜的数据，对国内高性能计算机的发展现状从总体性能、制造商、行业领域等方面进行了深入分析。中国TOP100新增2台系统，更新2台系统，前十名系统未变，第一名Linpack性能仍为487 PFlops。联想为系统数量冠军，联想、曙光和浪潮三强争霸的局面逐步演化为联想和浪潮的竞争。算力服务这一应用领域的占比依然超过70%，充分反映当前算力经济发展趋势。本文根据二十四届排行榜的性能数据，详细阐述了算力经济发展现状，并对未来中国大陆高性能计算机的发展趋势进行了分析预测。

题目：全球大模型开源战略的动因、影响与应对策略探析

作者：谭俊、程莹、刘志鹏

来源：大数据, 2025, 11(06):28-34.

摘要：讨论了AI大模型技术的变革，开源模式在加速创新、汇聚智慧、降低门槛方面的优势，以及开源对AI创新壁垒降低、技术普惠、多边协作的促进作用。同时，指出了开源模型在安全可控、合规伦理、供应链安全方面的风险，以及开源生态成熟度差异对产业格局的影响。最后，强调了分析全球大模型企业开源布局的动因、现状、影响及风险的重要性，特别是对中国的机遇与挑战，并提出了针对性策略。

题目：Leveraging Adaptive Evolutionary Optimization for Drug Molecular Design Involving Many Properties（面向多属性药物分子设计的自适应进化优化方法）

作者：X. Xia, X. Zeng, X. Zhang, C. Zheng, Y. Zhang and Y. Su

来源：Big Data Mining and Analytics（大数据挖掘与分析）, vol. 9, no. 1, pp. 143-159.

摘要：With the fast development of artificial intelligence, a lot of translation methods and search methods have been proposed to address molecular optimization problems in drug design, which enables this field to achieve remarkable progress. However, existing methods still encounter great difficulties in addressing problems involving more than three properties, since these problems pose stiff challenges to translation methods and search methods in terms of acquiring high-quality training data and balancing multiple properties, respectively. In this paper, we propose an adaptive evolutionary optimization framework to address the many-property molecular optimization problems (namely MaOMO). MaOMO adaptively identifies the property with the largest improvement potential in each iteration, which generates high-quality molecules as efficiently as possible by devoting more efforts to the property. Besides, MaOMO adopts a dynamic selection strategy to select molecules with large property improvement, good property diversity, and structure diversity. We investigate the performance of MaOMO framework on both benchmark and practical molecular optimization tasks, which involve the simultaneous optimization of four or more properties. Experimental results show that the proposed framework is superior to five state-of-the-art competitors, which achieves a success rate improvement of more than 20% on practical optimization tasks.

编者译：随着AI的快速发展，人们提出了许多翻译方法和搜索方法来解决药物设计中的分子优化问题，推动该领域取得显著进展。然而，现有方法在解决涉及超过三种性质的问题时仍面临巨大困难，因为这些问题分别给翻译方法在获取高质量训练数据和搜索方法在平衡多种性质方面带来了严峻挑战。本文提出了一种自适应进化优化框架，用于解决多属性分子优化问题（MaOMO）。MaOMO在每次迭代中自适应地识别具有最大改进潜力的性质，通过在该性质上投入更多精力，尽可能高效地生成高质量分子。此外，MaOMO采用动态选择策略来选择具有较大性质提升、良好性质多样性和结构多样性的分子。本文在涉及四个或更多性质同步优化的基准和实际分子优化任务上，测试了MaOMO框架的性能。实验结果表明，该框架优于五种最先进的竞争方法，在实际优化任务中成功率提升超过20%。

题目：委托量子计算验证方法研究综述

作者：袁梓萌、龙春、李婧、杨帆、付豫豪、魏金侠、万巍

来源：数据与计算发展前沿, 2025, 7(6): 55-67.

摘要：系统梳理和分析委托量子计算验证方法的研究进展与现状。本文调研了1994年至2025年主流会议与期刊的74篇文献，涵盖量子计算验证领域的核心成果与最新进展。以客户端量子能力需求为主线，将验证方法分为三类（弱量子能力、纠缠、计算假设），构建分类框架；通过对比通信模式、资源开销、容错性等维度，提炼方法演进规律与优劣。发现三类方法形成互补格局：弱量子方案最成熟但需客户端具备量子能力；纠缠方案仅要求经典客户端但需多服务器协同；计算假设方案通信最简单但依赖后量子密码学。资源开销从指数级降至近线性，但理论优化渐近瓶颈。研究重心正从理论优化转向应用落地，亟需跨平台实验验证与多方委托计算场景标准化协议。