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Reply to Cheng Hwee Ming

Juan Pablo Arroyo, Gerardo Gamba
2011 Physiology  
Cheng Hwee Ming on our recently published review in Physiology (1) .  ...  Cheng Hwee Ming accurately points out, "the overall neuro-hormonal, whole body response . . . is still essential to appreciate." Indeed, Dr.  ... 
doi:10.1152/physiol.00027.2011 fatcat:6whypvh63vgphkugyshw5c3xpq

The Cheng Communal Family: Social Organization and Neo-Confucianism in Yuan and Early Ming China

John W. Dardess
1974 Harvard Journal of Asiatic Studies  
The Ming founders were in no such debt to the Cheng, because in view of the Cheng default it was the Ming armies and not the Cheng that imposed order in P'u-chiang. 97 The weakened and vulnerable position  ...  In other respects too, the early Ming was a period of torment for the Cheng.  ... 
doi:10.2307/2718695 fatcat:crtt6riy55en5cifaywsdierae

Closure to "Earth Pressures with Sloping Backfill" by Yung‐Show Fang, Jiung‐Ming Chen, and Cheng‐Yu Chen

Yung‐Show Fang, Jiung‐Ming Chen, Cheng‐Yu Chen
1998 Journal of Geotechnical and Geoenvironmental Engineering  
doi:10.1061/(asce)1090-0241(1998)124:11(1153.x) fatcat:ltuiolzv2zexvczmywhpwhh5xm

Handbooks and local jurisdiction in Ming China. According to the sections on judicial matters in the «Shih-cheng lu» by Lü K'un, a handbook for magistrates

Dominiek Delporte
2002 Crime, History & Societies  
Handbooks and local jurisdiction in Ming China. According to the sections on ... agencies. Moral considerations are comparatively rare in his «Shih-cheng lu» 25 .  ...  This work offers a translation of one of the monographs on Ming law included in the «Ming-shih», the official dynastic history of the Ming. 3.  ... 
doi:10.4000/chs.412 fatcat:i44dcvcvl5btvebol42ci4d4im

Geometric Style Transfer [article]

Xiao-Chang Liu, Xuan-Yi Li, Ming-Ming Cheng, Peter Hall
2020 arXiv   pre-print
Neural style transfer (NST), where an input image is rendered in the style of another image, has been a topic of considerable progress in recent years. Research over that time has been dominated by transferring aspects of color and texture, yet these factors are only one component of style. Other factors of style include composition, the projection system used, and the way in which artists warp and bend objects. Our contribution is to introduce a neural architecture that supports transfer of
more » ... metric style. Unlike recent work in this area, we are unique in being general in that we are not restricted by semantic content. This new architecture runs prior to a network that transfers texture style, enabling us to transfer texture to a warped image. This form of network supports a second novelty: we extend the NST input paradigm. Users can input content/style pair as is common, or they can chose to input a content/texture-style/geometry-style triple. This three image input paradigm divides style into two parts and so provides significantly greater versatility to the output we can produce. We provide user studies that show the quality of our output, and quantify the importance of geometric style transfer to style recognition by humans.
arXiv:2007.05471v1 fatcat:uo34uik5jff3rpgd4ki255mzgq

Dependency Aware Filter Pruning [article]

Kai Zhao, Xin-Yu Zhang, Qi Han, Ming-Ming Cheng
2020 arXiv   pre-print
Cheng et al. [55] quantize CNNs with a predefined codebok.  ...  Cheng are with TKLNDST, CS, Nankai University, Tianjin, China.  ... 
arXiv:2005.02634v1 fatcat:c5nicqljtrau3igfcklnoqfqt4

A Millisecond-Anneal-Assisted Selective Fully Silicided (FUSI) Gate Process

Da-Wen Lin, Maureen Wang, Ming-Lung Cheng, Yi-Ming Sheu, Bennet Tarng, Che-Min Chu, Chun-Wen Nieh, Chia-Ping Lo, Wen-Chi Tsai, Rachel Lin, Shyh-Wei Wang, Kuan-Lun Cheng (+5 others)
2008 IEEE Electron Device Letters  
Cheng, Y.-M. Sheu, B. Tarng, C.-M. Chu, C.-W. Nieh, C.-P. Lo, W.-C. Tsai, R. Lin, S.-W. Wang, K.-L. Cheng, C.-M. Wu, M.-T. Lei, C.-C. Wu, and C. H.  ... 
doi:10.1109/led.2008.2001850 fatcat:puqd27iwxna3rfgc3io2p5n5e4

Visual Attention Network [article]

Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu
2022 arXiv   pre-print
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose
more » ... a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at
arXiv:2202.09741v3 fatcat:gqlascuu5vfwho73wy2msg4pym

Semi-Supervised Learning with Meta-Gradient [article]

Xin-Yu Zhang, Taihong Xiao, Haolin Jia, Ming-Ming Cheng, Ming-Hsuan Yang
2021 arXiv   pre-print
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning. We notice that most existing consistency-based approaches suffer from overfitting and limited model generalization ability, especially when training with only a small number of labeled data. To alleviate this issue, we propose a learn-to-generalize regularization term by utilizing the label information and optimize the problem in a meta-learning fashion. Specifically, we seek the pseudo labels
more » ... the unlabeled data so that the model can generalize well on the labeled data, which is formulated as a nested optimization problem. We address this problem using the meta-gradient that bridges between the pseudo label and the regularization term. In addition, we introduce a simple first-order approximation to avoid computing higher-order derivatives and provide theoretic convergence analysis. Extensive evaluations on the SVHN, CIFAR, and ImageNet datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.
arXiv:2007.03966v2 fatcat:wls2mkzn4nb7dhgyzk5dtjq4km

Concealed Object Detection [article]

Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao
2021 arXiv   pre-print
. • Ming-Ming Cheng is the corresponding author. 1.  ...  (E-mail:; • Ming-Ming Cheng is with the CS, Nankai University, Tianjin, China.  ... 
arXiv:2102.10274v2 fatcat:po667kryk5arbkk5e77ddqyg4y

Diamagnetic Response of Exciton Complexes in Semiconductor Quantum Dots

Ming-Fu Tsai, Hsuan Lin, Chia-Hsien Lin, Sheng-Di Lin, Sheng-Yun Wang, Ming-Cheng Lo, Shun-Jen Cheng, Ming-Chih Lee, Wen-Hao Chang
2008 Physical Review Letters  
We report measurements of diamagnetic shifts for different exciton complexes confined in small InAs quantum dots. The measured diamagnetic responses are sensitive to the number of carriers in the exciton complexes, with systematic differences between neutral excitons, biexcitons, and trions. Theoretical calculations suggest that such systematic differences arise from very different extents of electron and hole wave functions confined in small quantum dots. The measured magnetic response of
more » ... mb energies is found to vary with the cube of the wave function extent, and can be a sensitive probe to the electron-hole wave function asymmetry.
doi:10.1103/physrevlett.101.267402 pmid:19113787 fatcat:krd4azry4rdjlhdzkzaqob6y54

Pathway Integration and Visualization

Der-Ming Liou, Kai-Lung Tang, Yung-Wen Deng, Yu-Tai Wang, Cheng-Ming Wei, Ueng-Cheng Yang
2003 Genome Informatics Series  
doi:10.11234/gi1990.14.713 fatcat:haspwjsikzaqhp4wrajbi6jwa4

WebSeg: Learning Semantic Segmentation from Web Searches [article]

Qibin Hou, Ming-Ming Cheng, Jiangjiang Liu, Philip H.S. Torr
2018 arXiv   pre-print
In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods. To solve such a challenging problem, we leverage several low-level cues (such as saliency, edges, etc.) to help generate a proxy ground truth. Due to the diversity of web-crawled images, we
more » ... pate a large amount of 'label noise' in which other objects might be present. We design an online noise filtering scheme which is able to deal with this label noise, especially in cluttered images. We use this filtering strategy as an auxiliary module to help assist the segmentation network in learning cleaner proxy annotations. Extensive experiments on the popular PASCAL VOC 2012 semantic segmentation benchmark show surprising good results in both our WebSeg (mIoU = 57.0%) and weakly supervised (mIoU = 63.3%) settings.
arXiv:1803.09859v1 fatcat:onwapeatmnc4fgrzwtaq2xsckq

Does radiotherapy increase the risk of colorectal cancer among prostate cancer patients? A large population-based study

Chung-Han Ho, Kuo-Chen Cheng, Chien-Ming Chao, Chih-Cheng Lai, Shyh-Ren Chiang, Chin-Ming Chen, Kuang-Ming Liao, Jhi-Joung Wang, Po-Huang Lee, Chao-Ming Hung, Chi-Ming Tai, Chong-Chi Chiu
2020 Journal of Cancer  
Objective: The survival of prostate cancer (PC) patients after radiotherapy (RT) has improved over time, but it raises the debate of increased risk of secondary colorectal cancer (SCRC). This study aimed to assess whether RT for PC treatment increases the risk of SCRC in comparison with radical prostatectomy (RP). Methods: A population-based cohort of PC patients treated only with RT or only with RP between January 2007 and December 2015 was identified from the Taiwan Cancer Registry. The
more » ... nce rate of SCRC development was estimated using Cox regression model. Results: In this study, total 8,797 PC patients treated with either RT (n = 3,219) or RP (n =5,578). Patients subjected to RT were elder (higher percentage of 70≧years, p < 0.0001) and more advanced clinically (stage III: 22.90% vs. 11.87%; stage IV: 22.15% vs. 13.80%, p < 0.0001), compared to those subjected to RP. More patients subjected to RT had a much higher percentage of autoimmune disease (22.34% vs. 18.75%, p < 0.0001) and osteoarthritis and allied disorders (16.31% vs. 12.98%, p < 0.0001). Besides, RT patients had a higher percentage of underlying Crohn's disease (0.25% vs. 0.05%, p = 0.0230). Although almost all selected factors were not statistically significant, they presented the positive risk of SCRC for those under RP compared with those among RT. Besides, for PC patients in clinical stage I and II, patients with RP may have borderline significantly protective effects of SCRC compared with those under RT (stage I, HR: 0.14; 95% C.I.:0.01-1.39; p = 0.0929; stage II, HR: 1.92; 95% C.I.:0.93-3.95; p = 0.0775). Kaplan-Meier curves for a 3-year-period, which demonstrated no statistical difference in the risk of SCRC free between PC patients undergoing RT and RP (p = 0.9766). Conclusion: Whether or not pelvic RT for PC is associated with an increased risk for SCRC on a population-based level remains a matter of considerable debate. From a clinical perspective, these PC survivors should be counseled accordingly and received continued cancer surveillance with regular colonoscopy follow-up.
doi:10.7150/jca.44726 pmid:33033503 pmcid:PMC7532509 fatcat:zslrpnxgz5bkjd7tewwwc7o2ci

Self-Erasing Network for Integral Object Attention [article]

Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng
2018 arXiv   pre-print
Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy raises one key problem that attention regions will gradually expand to non-object regions as training iterations continue, which significantly decreases the quality of the produced attention maps. To tackle such an issue as well as promote the quality of object attention, we introduce a simple yet effective
more » ... Network (SeeNet) to prohibit attentions from spreading to unexpected background regions. In particular, SeeNet leverages two self-erasing strategies to encourage networks to use reliable object and background cues for learning to attention. In this way, integral object regions can be effectively highlighted without including much more background regions. To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models. Experiments on Pascal VOC well demonstrate the superiority of our SeeNet over other state-of-the-art methods.
arXiv:1810.09821v1 fatcat:n7ekpypatrhxbdptks7um6vzhi
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