182 research outputs found
Bei ji qian jin yao fang: 30 juan. v.1
No full text孫思邈撰 ; 林億校.框21.9x15.1公分, 13行23字, 白口, 左右雙邊, 雙黑魚尾, 版心中鐫書名, 卷次.綫裝, 2函.江戶醫學北宋栞本景摹開雕.Sun Simiao zhuan ; Lin Yi jiao.Kuang 21.9 x 15.1 gong fen, 13 hang 23 zi, bai kou, zuo you shuang bian, shuang hei yu wei, ban xin zhong juan shu ming, juan ci.Xian zhuang, 2 han.Jianghu yi xue BeiSong kan ben ying mo kai diao
Jing yuan da de ben qian jin yi fang: san shi juan
No full text[孫思邈撰] ; 林億...[et al.]校正.綫裝.框20.7x13.2公分, 13行23字, 小字雙行. 黑口, 四周雙邊, 順黑魚尾. 版心中鐫"翼方"及卷次, 下鐫葉次.內封背頁牌記印"光緖戊寅[1878]上海印行獨山莫繩孫補署檢" ; 卷末刻有"文政十二年[1829]重彫元大德刊本", 並鐫有原牌記: "大德丁未良月梅溪書院刻梓"(Copy 2)鈐"莊兆祥印", "莊兆祥"Xian zhuang.Kuang 20.7 x 13.2 gong fen, 13 hang 23 zi, xiao zi shuang hang. Hei kou, si zhou shuang bian, shun hei yu wei. Ban xin zhong juan "Yi fang" ji juan ci, xia juan ye ci.Detailed notes in vernacular field only.Series title supplied by cataloguer.[Sun Simiao zhuan] ; Lin Yi ...[et al.] jiao zheng.(Copy 2) qian "Zhuang Zhaoxiang yin", "Zhuang Zhaoxiang
Bei ji qian jin yao fang: san shi juan. v.1
No full text[孫思邈撰] ; 林億...[et al.]校正 .綫裝.框20.9x14.7公分, 13行23字. 白口, 間中有黑口, 左右雙邊, 順黑魚尾. 版心中鐫"千金要方"及卷次, 下鐫葉次及刻工.書名背頁刻"江戶醫學影北宋本, 光緖戊寅[1878]夏五購自東瀛, 印於上海, 長洲黃學熙記".目錄卷端下刻"金澤文庫" ; 書末有牌記刻"嘉永紀元江戶醫學北宋槧本景摹開雕"此書為日本嘉永2年[1849]江戶醫學據北宋本影刻, 後黃學熙得到書版, 重新刷印.《中國中醫古籍總目》(03309)著錄.鈐"莊兆祥印", "莊兆祥".Xian zhuang.Kuang 20.9 x 14.7 gong fen, 13 hang 23 zi. Bai kou, jian zhong you hei kou, zuo you shuang bian, shun hei yu wei. Ban xin zhong juan "Qian jin yao fang" ji juan ci, xia juan ye ci ji ke gong.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Detailed notes in vernacular field only.[Sun Simiao zhuan] ; Lin Yi ...[et al.] jiao zheng .Qian "Zhuang Zhaoxiang yin", "Zhuang Zhaoxiang"
The Effects of Modified Simiao Decoction in the Treatment of Gouty Arthritis: A Systematic Review and Meta-Analysis
No full textThe modified Simiao decoctions (MSD) have been wildly applied in the treatment of gouty arthritis in China. However, the evidence needs to be evaluated by a systematic review and meta-analysis. After filtering, twenty-four randomised, controlled trials (RCTs) comparing the effects of MSD and anti-inflammation medications and/or urate-lowering therapies in patients with gouty arthritis were included. In comparison with anti-inflammation medications, urate-lowering therapies, or coadministration of anti-inflammation medications and urate-lowering therapies, MSD monotherapy significantly lowered serum uric acid (p<0.00001, mean difference = −90.62, and 95% CI [−128.38, −52.86];p<0.00001, mean difference = −91.43, and 95% CI [−122.38, −60.49];p=0.02, mean difference = −40.30, and 95% CI [−74.24, −6.36], resp.). Compared with anti-inflammation medications and/or urate-lowering therapies, MSD monotherapy significantly decreased ESR (p<0.00001; mean difference = −8.11; 95% CI [−12.53, −3.69]) and CRP (p=0.03; mean difference = −3.21; 95% CI [−6.07, −0.36]). Additionally, the adverse effects (AEs) of MSD were fewer (p<0.00001; OR = 0.08; 95% CI [0.05, 0.16]). MSD are effective in the treatment of gouty arthritis through anti-inflammation and lowering urate. However, the efficacy of MSD should be estimated with more RCTs.</jats:p
New publication "The Temple and its Actors: Religious Institutions and Urban Communities in China from the Ming and Qing Dynasties to the Republican Era"
No full textLü Min (Marianne Bujard), Lu Kang (Luca Gabbiani) (eds.), Xianghuo xinyuan: Ming Qing zhi Minguo shiqi Zhongguo chengshi de simiao yu shimin 香火新緣：明清至民國時期中國城市的寺廟與市民 (The Temple and its Actors: Religious Institutions and Urban Communities in China from the Ming and Qing Dynasties to the Republican Era), Beijing, Zhongxin chuban jituan, 2018 ISBN: 9787508673257. This book is the outcome of an international conference which took place in 2009 at Beijing Normal University (Beijing shifan daxue)...
Adversarial Learning for Image-to-Image Generative Creativity
No full textAchieving generative creativity in the context of visual data, i.e. the generation of novel and valuable images, is a long-standing goal in computer vision and artificial intelligence. Generative adversarial networks (GANs) are prominent deep generative models that can successfully generate visually-appealing images. However, the generated images are mostly simple memorisation or imitation of training samples, which exhibits limited generative creativity. To obtain higher-degree generative creativity, we focus on more challenging image-to-image generation tasks, in which the generated images are not only more practically valuable, but also more distinct from existing data. The challenges of achieving image-to-image generative creativity lie in three aspects: whether the generated images 1) are truly useful, especially for critical applications (e.g. in the field of medical imaging), and 2) can demonstrate a clear difference from training samples, and 3) are varied and diverse for one input image, which is a natural requirement for many image generation tasks. In this thesis, we aim to develop deep conditional adversarial networks for challenging image-to-image generation tasks, each of which respectively exhibits one type of image-to-image generative creativity. We make the following contributions. First, we propose EnrichGAN for fast compressed sensing magnetic resonance imaging (CS-MRI) reconstruction that exhibits enrichment creativity. We demonstrate that EnrichGAN qualitatively and quantitatively outperforms various conventional and state-of-the-art methods, with a much faster processing time that enables real-time applications. Second, we propose SimGAN for semantic image manipulation. It requires learning good mappings between visual and text features. We show that SimGAN achieves superior results on this challenging image-to-image generation task that demonstrates high-level transformative creativity. Finally, we propose DesignGAN for automating the process of shape-oriented bionic design. It requires learning to combine features of images from different domains, in an unsupervised fashion. We demonstrate that Design- GAN learns to achieve image-to-image combinatorial creativity.Open Acces
Qian jin fang yan yi: [30 juan]. v.1
No full text張路玉[張璐(fl. 1670-1705)]著.框17.9x14公分, 10行20字, 白口, 四周單邊, 雙黑魚尾, 版心中鐫書名, 卷次.綫裝, 3函."香港中文大學圖書館中國古籍庫"提供電子版.Kuang 17.9 x 14 gong fen, 10 hang 20 zi, bai kou, si zhou shuang bian, shuang hei yu wei, ban xin zhong juan shu ming, juan ci.Xian zhuang, 3 han.Zhang Luyu [Zhang Lu (fl. 1670-1705)] zhu."Xianggang Zhong wen da xue tu shu guan Zhongguo gu ji ku" ti gong dian zi ban
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
No full textLarge language models (LLMs) have made impressive progress in natural
language processing. These models rely on proper human instructions (or
prompts) to generate suitable responses. However, the potential of LLMs are not
fully harnessed by commonly-used prompting methods: many human-in-the-loop
algorithms employ ad-hoc procedures for prompt selection; while auto prompt
generation approaches are essentially searching all possible prompts randomly
and inefficiently. We propose Evoke, an automatic prompt refinement framework.
In Evoke, there are two instances of a same LLM: one as a reviewer
(LLM-Reviewer), it scores the current prompt; the other as an author
(LLM-Author), it edits the prompt by considering the edit history and the
reviewer's feedback. Such an author-reviewer feedback loop ensures that the
prompt is refined in each iteration. We further aggregate a data selection
approach to Evoke, where only the hard samples are exposed to the LLM. The hard
samples are more important because the LLM can develop deeper understanding of
the tasks out of them, while the model may already know how to solve the easier
cases. Experimental results show that Evoke significantly outperforms existing
methods. For instance, in the challenging task of logical fallacy detection,
Evoke scores above 80, while all other baseline methods struggle to reach 20
Design and control of multifunctional sorting and training platform based on PLC control
No full text- …
