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The Lost Garden is an eloquent portrait of the losses incurred as we struggle to hold on to our passions. The novel begins with the family of Zhu Yinghong, whose father, Zhu Zuyan, was imprisoned in the early days of Chiang Kai-shek's rule. Zhu Zuyan spends his days luxuriating in his Lotus Garden, which he builds according to his own desires. Forever under suspicion, he indulges as much as he can in circumscribed pleasures, though they drain the family fortune. Eventually the entire household is sold, including the Lotus Garden. The novel then swings to modern-day Taipei, where Zhu Yinghong falls for Lin Xigeng, a real estate tycoon and playboy. Their cat-and-mouse courtship builds against the extravagant banquets and decadent entertainments of Taipei's wealthy businessmen. Though the two ultimately marry, their high-styled romance dulls over time, leading to a dangerous, desperate quest to reclaim the enchantment of the Lotus Garden.

Gardens --- Gardening --- International Writing Program participants --- Li, Ang, 1952 --- -Taiwan --- Taiwan Sheng zheng fu --- Tʻai-wan sheng cheng fu --- Taiwan xing zheng zhang guan gong shu --- Tʻai-wan hsing cheng chang kuan kung shu --- Taiwan Sheng xing zheng zhang guan gong shu --- Tʻai-wan sheng hsing cheng chang kuan kung shu --- Tʻai-wan --- Taĭvan --- Formosa --- Taiwan Sōtokufu --- Government-General of Taiwan --- Taiwan sheng --- Tʻai-wan sheng --- Taiwan Provincial Government --- Taiwan zong du fu --- Tʻai-wan tsung tu fu --- Xiaoliuqiu --- 臺灣 --- 台灣 --- Тайвань --- Tajvan --- Тайуан --- Taĭuan --- Tayiwani --- Taywan --- Taivanas --- Taiwana --- Taihuan --- Тайван --- China (Republic : 1949- ) --- History --- Taiwan

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U.S. air superiority, a cornerstone of U.S. deterrence efforts, is being challenged by competitors—most notably, China. The spread of machine learning (ML) is only enhancing that threat. One potential approach to combat this challenge is to more effectively use automation to enable new approaches to mission planning. The authors of this report demonstrate a prototype of a proof-of-concept artificial intelligence (AI) system to help develop and evaluate new concepts of operations for the air domain. The prototype platform integrates open-source deep learning frameworks, contemporary algorithms, and the Advanced Framework for Simulation, Integration, and Modeling—a U.S. Department of Defense–standard combat simulation tool. The goal is to exploit AI systems' ability to learn through replay at scale, generalize from experience, and improve over repetitions to accelerate and enrich operational concept development. In this report, the authors discuss collaborative behavior orchestrated by AI agents in highly simplified versions of suppression of enemy air defenses missions. The initial findings highlight both the potential of reinforcement learning (RL) to tackle complex, collaborative air mission planning problems, and some significant challenges facing this approach.

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Over the past few years, commercial space services have significantly increased in capability and capacity in many missions of interest to the U.S. Space Force (USSF). As the USSF considers incorporating such commercial space services into its missions, it needs a principled and flexible assessment framework for evaluating how commercial contributions affect the performance and resilience of various USSF missions. This report describes the resilience assessment framework that RAND Project AIR FORCE developed to assess the potential impact of select commercial services on USSF space mission performance. The framework includes four components to help analysts consider the additional mission performance and mission resilience that a proposed commercial service could provide. The prospect of incorporating commercial capabilities into defense missions also raises the question of trustworthiness and information assurance. The assessment framework includes a subframework focused on grading information contributions from commercial services for trustworthiness. To demonstrate how the framework can be tailored to diverse missions by specifying the relevant mission assets or infrastructure, the commercial services available, and the mission performance measures, the authors apply the framework to two example missions: tactical intelligence, surveillance, and reconnaissance and data transmit and receive network. They then discuss their findings and propose several recommendations for the USSF to fully leverage the benefits of evaluating commercial capabilities with the resilience assessment framework.

Astronautics, Military --- Artificial satellites --- United States.