When Chen Zhu heads to Hong Kong from mainland China every month, there's one place she always goes: Hunter Bookstore. Founded four years ago by a former councillor, the independent bookshop has earned a reputation for stocking what authorities would consider sensitive books - accounts of Hong Kong's pro-democracy campaigners to famous allegories on authoritarianism, such as George Orwell's Animal Farm or Hajime Isayama's Manga series Attack on Titan. It's a short journey from Zhu's home in Guangzhou on China's southern coast to the crowded street in Hong Kong's Sham Shui Po district, where Hunter is sandwiched between fabric sellers, garages and trendy cafes. But, for the 23-year-old, it is also a passage into a different, freer world - where she can find answers to forbidden questions. When she learned the bookshop had been raided by police in late June, Zhu broke into tears: I was devastated.

Chinese tech firms including Bytedance are driving demand for Hong Kong's data centers. Chinese tech firms, from Bytedance to artificial intelligence startups, are driving demand for Hong Kong's data centers, helping push lease prices up by nearly double this year. The AI boom, fueled in part by the success of DeepSeek, has prompted more Chinese firms to use Hong Kong as a springboard to test models and expand overseas. Deals for digital infrastructure accelerated in the last quarter, driving up prices for data center capacity on a per-kilowatt basis, according to Structure Research, a data center consultancy. Chinese "hyperscalers," including Alibaba Group Holding and Tencent Holdings, are increasing their capacity in the city, said Jason Zhou, senior analyst with the research firm.

A Border Force officer and his handler who worked for Chinese intelligence in the UK have been jailed. Chi Leung Peter Wai, 40, was sentenced to 10 years and Chung Biu Bill Yuen, 65, given an eight year term after being found guilty of assisting a foreign intelligence service, an offence under the National Security Act. Wai used his position as a Border Force officer to access to the Home Office computer system to track Hong Kong dissidents in the UK, was also convicted of misconduct in public office. The judge Mrs Justice Cheema-Grubb told the men that their actions threaten the sovereignty of the state during sentencing remarks at the Old Bailey on Thursday. The dual Chinese-British nationals were found guilty after a trial last month.

Hong Kong - Claw machine lover Neiki Lee carefully lowers the metal jaws of a crane with a joystick into a pool of prizes, only to have the small toy slip from its clutches again and again. Dozens of stores filled with claw machines have sprung up on streets and in malls across Hong Kong's finance hub in recent years, promising players a treasure trove of prizes and a sense of fulfillment. The colorfully lit machines, often seen drawing people like moths to a flame, have come under regulatory scrutiny this month, as officials raised addiction concerns over the seemingly harmless games. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

A 17-year-old British sailor saved Simon from the Hong Kong docks when he was likely a year old. Breakthroughs, discoveries, and DIY tips sent six days a week. One day in March of 1948, George Hickinbottom, a British sailor, was walking around the docks of Stonecutters Island in Hong Kong. When the 17-year-old spotted a small black-and-white tuxedo cat, barely out of kittenhood, he decided to smuggle the hungry, scrawny animal aboard his ship, the HMS . Hickinbottom didn't get in trouble.

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Cardinal Stephen Chow, the bishop of Hong Kong, speaks during Mass in Hong Kong in November 2023. During the opening Mass a three-day event to draft guidelines for the clergy's use of artificial intelligence in the region Asia, he described AI as a gift from God. | REUTERS Catholic bishops and priests from across Asia are set to conclude a three-day event in Hong Kong on Friday, during which they drafted guidelines for the clergy's use of artificial intelligence in the region. The Federation of Asian Bishops -- a 55-year-old institution that includes representatives from across the region, including Indonesia, Taiwan, Sri Lanka and Japan -- discussed AI and its impact on humanity, the church, and how it can serve as a tool to conduct scripture searches. They also discussed the principles for use of AI in evangelization. The theme of the meetings was a call to embrace AI responsibly.

Neural prediction offers a promising approach to forecasting the individual variability of neurocognitive functions and disorders and providing prognostic indicators for personalized invention. However, it is challenging to translate neural predictive models into medical artificial intelligent applications due to the limitations of domain shift and label scarcity. Here, we propose the directed evolution model (DEM), a novel computational model that mimics the trial-and-error processes of biological directed evolution to approximate optimal solutions for predictive modeling tasks. We demonstrated that the directed evolution algorithm is an effective strategy for uncertainty exploration, enhancing generalization in reinforcement learning. Furthermore, by incorporating replay buffer and continual backpropagate methods into DEM, we provide evidence of achieving better trade-off between exploitation and exploration in continuous learning settings. We conducted experiments on four different datasets for children with cochlear implants whose spoken language developmental outcomes vary considerably on the individual-child level. Preoperative neural MRI data has shown to accurately predict the post-operative outcome of these children within but not across datasets. Our results show that DEM can efficiently improve the performance of cross-domain pre-implantation neural predictions while addressing the challenge of label scarcity in target domain.

Multi-agent reinforcement learning (MARL) has been increasingly adopted in many real-world applications. While MARL enables decentralized deployment on resource-constrained edge devices, it suffers from severe non-stationarity due to the synchronous updates of agent policies. This non stationarity results in unstable training and poor policy con vergence, especially as the number of agents increases. In this paper, we propose RELED, a scalable MARL framework that integrates large language model (LLM)-driven expert demonstrations with autonomous agent exploration. RELED incorporates a Stationarity-Aware Expert Demonstration module, which leverages theoretical non-stationarity bounds to enhance the quality of LLM-generated expert trajectories, thus providing high reward and training-stable samples for each agent. Moreover, a Hybrid Expert-Agent Policy Optimization module adaptively balances each agent's learning from both expert-generated and agent-generated trajectories, accelerating policy convergence and improving generalization. Extensive experiments with real city networks based on OpenStreetMap demonstrate that RELED achieves superior performance compared to state-of-the-art MARL methods.

Aspect category sentiment analysis (ACSA) has achieved remarkable progress with large language models (LLMs), yet existing approaches primarily emphasize sentiment polarity while overlooking the underlying emotional dimensions that shape sentiment expressions. This limitation hinders the model's ability to capture fine-grained affective signals toward specific aspect categories. To address this limitation, we introduce a novel emotion-enhanced multi-task ACSA framework that jointly learns sentiment polarity and category-specific emotions grounded in Ekman's six basic emotions. Leveraging the generative capabilities of LLMs, our approach enables the model to produce emotional descriptions for each aspect category, thereby enriching sentiment representations with affective expressions. Furthermore, to ensure the accuracy and consistency of the generated emotions, we introduce an emotion refinement mechanism based on the Valence-Arousal-Dominance (VAD) dimensional framework. Specifically, emotions predicted by the LLM are projected onto a VAD space, and those inconsistent with their corresponding VAD coordinates are re-annotated using a structured LLM-based refinement strategy. Experimental results demonstrate that our approach significantly outperforms strong baselines on all benchmark datasets. This underlines the effectiveness of integrating affective dimensions into ACSA.