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"Mah jong."
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Mahjong : house rules from across the Asian diaspora
A step-by-step guide to Mahjong for new and seasoned players alike. For many third-culture kids in the Asian diaspora, the sight and sound of clacking mahjong tiles conjures deep-seated memories. Some learned to play alongside elders, while others watched from afar. Maybe you only distantly know of the game through movies or articles about pop-up mahjong nights. Whether you are looking to reconnect with the game, pick up a new hobby, or introduce others to a favorite pastime, Mahjong is a step-by-step guide for new and seasoned players alike. With vibrant photography and detailed instructional diagrams, author Nicole Wong walks you through gameplay, just how her grandfather would have. In addition, Mahjong delves into the strategy, history, and design of the game and discusses various popular styles of play (American mah-jongg, Japanese riichi mahjong, and more). A bonus essay also breaks down the mahjong hands in the pivotal scene from the 2018 hit film Crazy Rich Asians, and snippets of the author's family history are sprinkled throughout, telling a story of diaspora through mahjong and showcasing mahjong culture, past and present. With Mahjong as your guide, learn the history and evolution of this beautiful game while you connect with friends, family, and culture. You may even write your own house rules -- just don't forget the snacks!
BOOK
A study on Mahjong intervention to improve cognitive impairment in patients with schizophrenia: a pilot, single-blind, randomized, controlled trial
Objective
Mahjong, a traditional Chinese tile-based game, has been widely reported to be closely associated with better cognitive function. However, its effects on the cognitive function of patients with schizophrenia have not yet been studied.
Method
In a pilot study, 49 patients diagnosed with schizophrenia were randomly assigned to the intervention group (Mahjong combined with standard treatment) and the control group (standard treatment). The intervention group engaged in cognitive training through Mahjong for 2 h per day, 4 days per week for 12 weeks. Primary cognitive outcomes were assessed using Cambridge Neuropsychological Test Automated Battery (CANTAB), while secondary outcomes include quality of life, clinical symptoms, anhedonia, treat side effects, and personal and social functioning. Assessments were conducted at baseline (T0), the 4th week (T1), the 8th week (T2), and the 12th week (T3).
Results
The intervention group exhibited progressive improvements in both reaction time and movement time throughout the study. No significant differences were found between the intervention and control groups regarding visual memory, novel learning, strategy utilization, spatial memory performance or complex visual task accuracy. The intervention group demonstrated gradual improvements in quality of life, whereas no significant changes were noted in other secondary outcomes.
Conclusion
While this exploratory study suggests that Mahjong intervention may benefit certain cognitive functions and quality of life in patients with schizophrenia, these findings should be interpreted with caution. Further research with larger, more diverse samples and longer intervention is necessary to confirm and extend these findings.
Trial registration
The trial is registered with
https://www.chictr.org.cn/
under registration number ChiCTR2400080268 on January 25th, 2024.
Journal Article
The complete book of mah jongg : an illustrated guide to the Asian, American, and international styles of play
This \"handbook includes over 150 full-color photographs and illustrations to introduce players to [mah jong]. This is the first mahjong book to fully cover the Cantonese or 'old rules' game. It also includes all of the most popular variations, such as the Shanghai game, the 16-tile or Taiwanese game, and the 12-tile game. Included are the rules, tactics, techniques, strategy and the rituals only the most experienced players know\"--Amazon.com.
BOOK
Association of playing cards/mahjong with all-cause mortality in older adults: a cohort study
Background
Engagement in leisure activities such as playing cards or mahjong has been linked to cognitive and physical benefits among older adults. However, the relationship between playing cards/mahjong and all-cause mortality remains unclear. This study aims to explore the association between playing cards/mahjong and all-cause mortality in a cohort of older Chinese adults.
Methods
We analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) collected between 2002 and 2018. A total of 29,254 participants aged 65 or older were included in the study. Participants were categorized based on their frequency of playing cards/mahjong: almost every day, at least once a week, at least once a month or sometimes, and never. Cox proportional hazards models were applied to assess the association between playing cards/mahjong and all-cause mortality. Interaction and sensitivity analyses were conducted to explore potential effect modifications and the robustness of the findings.
Results
During a mean follow-up of 4.6 years, 7,651 participants died. Compared to participants who never played cards/mahjong, those who played almost every day had a significantly lower risk of all-cause mortality (HR = 0.88; 95% CI, 0.82–0.95). Participants who played at least once a week (HR = 0.84; 95% CI, 0.78–0.91) similarly demonstrated reduced mortality. Participants who consistently played cards/mahjong at least once a week at both baseline and follow-up had a significantly lower all-cause mortality risk compared to never-players (HR = 0.67; 95% CI: 0.52–0.86). Interaction analysis revealed that the protective effect of playing cards/mahjong was more pronounced in participants aged 80 or older (P-interaction < 0.001). Sensitivity analyses confirmed the robustness of these associations.
Conclusion
Older adults who regularly play cards/mahjong have a lower risk of all-cause mortality, especially in the oldest groups. These leisure activities may promote longevity, but further research is needed to understand why.
Journal Article
CRL4.sup.Mahj E3 ubiquitin ligase promotes neural stem cell reactivation
The ability of neural stem cells (NSCs) to transit between quiescence and proliferation is crucial for brain development and homeostasis. Drosophila Hippo pathway maintains NSC quiescence, but its regulation during brain development remains unknown. Here, we show that CRL4.sup.Mahj, an evolutionarily conserved E3 ubiquitin ligase, is essential for NSC reactivation (exit from quiescence). We demonstrate that damaged DNA-binding protein 1 (DDB1) and Cullin4, two core components of Cullin4-RING ligase (CRL4), are intrinsically required for NSC reactivation. We have identified a substrate receptor of CRL4, Mahjong (Mahj), which is necessary and sufficient for NSC reactivation. Moreover, we show that CRL4.sup.Mahj forms a protein complex with Warts (Wts/large tumor suppressor [Lats]), a kinase of the Hippo signaling pathway, and Mahj promotes the ubiquitination of Wts. Our genetic analyses further support the conclusion that CRL4.sup.Mahj triggers NSC reactivation by inhibition of Wts. Given that Cullin4B mutations cause mental retardation and cerebral malformation, similar regulatory mechanisms may be applied to the human brain.
Journal Article
The little book of mahjong : learn how to play, score, and win
\"Disconnect from a device-oriented world and rediscover the classic Chinese game of mahjong--while improving focus, creativity and stimulating memory--with this comprehensive yet gifty guide that includes all the basics of the game, tips and techniques on how to play, and strategies to win both international mahjong and American mahjong. The classic game of mahjong was created over 2,000 years ago during the Qing Dynasty in China, but recently, it's experiencing a renaissance. No longer reserved for older players, mahjong is being rediscovered by millennials and is recognized for its ability to improve brain function, and reduce symptoms of cognitive diseases like dementia. Learning any new game can be difficult, especially one as complex as mahjong, which includes over dozens of tiles with different meanings. But whether you are a complete novice or a casual player looking to brush up on mahjong skills and techniques, The Little Book of Mahjong can help you get ahead in this classic game, whether you are playing international mahjong or American mahjong. With a comprehensive breakdown on all of the various tiles involved in the different versions of the game, explanations of what each tile means, detailed information on scoring, tips on how to improve your skills, and strategies for winning, this guide is perfect for mahjong players both new and old. Including a brief history of the game and its significance as well as full-color photos of game pieces and game objectives, you'll become a mahjong master in no time\"-- Provided by publisher.
Book
Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring effective long-term decision-making amidst randomness and hidden information. Through the efforts of AI researchers, several impressive Mahjong AI agents have already achieved performance levels comparable to those of professional human players; however, these agents are often treated as black boxes from which few insights can be gleaned. This paper introduces Mxplainer, a parameterized search algorithm that can be converted into an equivalent neural network to learn the parameters of black-box agents. Experiments on both human and AI agents demonstrate that Mxplainer achieves a top-three action prediction accuracy of over 92% and 90%, respectively, while providing faithful and interpretable approximations that outperform decision-tree methods (34.8% top-three accuracy). This enables Mxplainer to deliver both strategy-level insights into agent characteristics and actionable, step-by-step explanations for individual decisions.
Journal Article
Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost
Bloody Mahjong is a kind of mahjong. It is very popular in China in recent years. It not only has the characteristics of mahjong's conventional state space, huge hidden information, complicated rules, and large randomness of hand cards but also has special rules such as Change three, Hu must lack at least one suit, and Continue playing after Hu. These rules increase the difficulty of research. These special rules are used as the input of the deep learning DenseNet model. DenseNet is used to extract the Mahjong situation features. The learned features are used as the input of the classification algorithm XGBoost, and then the XGBoost algorithm is used to derive the card strategy. Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of high‐dimensional sparse features. In the case of fewer training rounds, accuracy of the model can still reach 83%. In the games against real people, it plays like human.
Journal Article
Official International Mahjong: A New Playground for AI Research
Games have long been benchmarks and testbeds for AI research. In recent years, with the development of new algorithms and the boost in computational power, many popular games played by humans have been solved by AI systems. Mahjong is one of the most popular games played in China and has been spread worldwide, which presents challenges for AI research due to its multi-agent nature, rich hidden information, and complex scoring rules, but it has been somehow overlooked in the community of game AI research. In 2020 and 2022, we held two AI competitions of Official International Mahjong, the standard variant of Mahjong rules, in conjunction with a top-tier AI conference called IJCAI. We are the first to adopt the duplicate format in evaluating Mahjong AI agents to mitigate the high variance in this game. By comparing the algorithms and performance of AI agents in the competitions, we conclude that supervised learning and reinforcement learning are the current state-of-the-art methods in this game and perform much better than heuristic methods based on human knowledge. We also held a human-versus-AI competition and found that the top AI agent still could not beat professional human players. We claim that this game can be a new benchmark for AI research due to its complexity and popularity among people.
Journal Article