Optimal harvesting under annuity and compound interest laws: economic-ecological trade-offs in a logistic growth model
研究通过比较年金和复利两种利息原则,利用最优控制理论在Logistic增长模型中寻找平衡经济效益与生态可持续性的最佳捕捞策略。
研究通过比较年金和复利两种利息原则,利用最优控制理论在Logistic增长模型中寻找平衡经济效益与生态可持续性的最佳捕捞策略。
为解决临床领域缺乏多模态、多语言和时间序列基准的问题,MMTClinic通过结合文本、医学图像及生理信号,并使用五种语言的30,000个问答对来评估大型语言模型在复杂推理与问答任务中的表现。
研究评估了证据感知检索在RAG中的下游效用,通过分析其在不同角色下的表现,发现该方法虽改变检索排名,但对提升生成质量的可靠性有限。
本文通过构建门级实现框架,解决了缺乏实际量子硬件上执行的懒散量子行走搜索问题,并分析了其资源需求。
This work addresses the fundamental challenge of efficiently integrating classical cryptanalytic evidence with quantum verification in hybrid cryptanalysis. We propose a capability-adaptive unified framework that synergistically combines linear, differential, and side-channel analyses. By constructing a mathematical model for candidate space reduction, the framework establishes a theoretical linkage between spatial compression and the complexity of quantum verification, while incorporating a Hamiltonian formulation to ensure physical realizability. Experimental results demonstrate that the initial key hypothesis space of 4,096 candidates is reduced to just 13 (a compression rate of 99.683%), and the number of Grover iterations required for verification drops from 50 to 2—yielding an approximate 25-fold efficiency gain—with a target state success probability of 94.53%.
研究通过比较年金和复利两种利息原则,利用最优控制理论在Logistic增长模型中寻找平衡经济效益与生态可持续性的最佳捕捞策略。
为解决临床领域缺乏多模态、多语言和时间序列基准的问题,MMTClinic通过结合文本、医学图像及生理信号,并使用五种语言的30,000个问答对来评估大型语言模型在复杂推理与问答任务中的表现。
研究评估了证据感知检索在RAG中的下游效用,通过分析其在不同角色下的表现,发现该方法虽改变检索排名,但对提升生成质量的可靠性有限。
本文通过构建门级实现框架,解决了缺乏实际量子硬件上执行的懒散量子行走搜索问题,并分析了其资源需求。
This work addresses the fundamental challenge of efficiently integrating classical cryptanalytic evidence with quantum verification in hybrid cryptanalysis. We propose a capability-adaptive unified framework that synergistically combines linear, differential, and side-channel analyses. By constructing a mathematical model for candidate space reduction, the framework establishes a theoretical linkage between spatial compression and the complexity of quantum verification, while incorporating a Hamiltonian formulation to ensure physical realizability. Experimental results demonstrate that the initial key hypothesis space of 4,096 candidates is reduced to just 13 (a compression rate of 99.683%), and the number of Grover iterations required for verification drops from 50 to 2—yielding an approximate 25-fold efficiency gain—with a target state success probability of 94.53%.