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15,055 results on '"Yang Yun"'

275. Recruiting the most vulnerable : what explains the use of child soldiers?

Author

Yang, Yun Jung

Subjects
JA Political science (General)
Abstract

What factors influence the use of child soldiers in armed conflict? The recruitment of child soldiers is widespread in modern conflict, with at least 300,000 children believed to be serving as soldiers until recent years. A growing body of analysis looks at this question but there is a limited amount of systematic research. The existing literature focuses on factors such as poverty, the youth population, and the availability of lightweight weapons as factors behind child recruitment, but other explanations seem likely. To better understand this important issue, this dissertation examines two under-examined factors - international humanitarian regulation and external support - and uses quantitative analyses to test resulting hypotheses. The first chapter addresses the impact of the international humanitarian regulations on governments and rebel groups' child recruitment. An important finding is that the existing regulations do not seem to reduce governments and rebel groups' forced recruitment. The second and the third chapters focus solely on rebel groups' child recruitment. These two chapters suggest external support is a crucial factor in rebel groups' child recruitment. More specifically, the second chapter examines how different types of external support can affect rebel groups' child recruitment and argues that rebel groups that receive territorial support are more likely to recruit children than rebels that do not. The third chapter investigates how two different types of external supporters can influence child soldiering by rebel groups. The results indicate that rebel groups that receive support from different rebel groups are more likely to recruit children than those that do not. Overall, this dissertation contributes to debates on child soldiering, rebel group dynamics, international humanitarian regulation, and related topics. Doing so, it sheds light on previously unexamined factors behind this important phenomenon, providing contributions to both theory and policy.

Published
2022

276. Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit

Author

Li, Ke, Yang, Yun, and Narisetty, Naveen N.

Subjects
Computer Science - Machine Learning, Statistics - Methodology
Abstract

In this paper, we consider the multi-armed bandit problem with high-dimensional features. First, we prove a minimax lower bound, $\mathcal{O}\big((\log d)^{\frac{\alpha+1}{2}}T^{\frac{1-\alpha}{2}}+\log T\big)$, for the cumulative regret, in terms of horizon $T$, dimension $d$ and a margin parameter $\alpha\in[0,1]$, which controls the separation between the optimal and the sub-optimal arms. This new lower bound unifies existing regret bound results that have different dependencies on T due to the use of different values of margin parameter $\alpha$ explicitly implied by their assumptions. Second, we propose a simple and computationally efficient algorithm inspired by the general Upper Confidence Bound (UCB) strategy that achieves a regret upper bound matching the lower bound. The proposed algorithm uses a properly centered $\ell_1$-ball as the confidence set in contrast to the commonly used ellipsoid confidence set. In addition, the algorithm does not require any forced sampling step and is thereby adaptive to the practically unknown margin parameter. Simulations and a real data analysis are conducted to compare the proposed method with existing ones in the literature.

Published
2021

277. Statistical Inference for Bayesian Risk Minimization via Exponentially Tilted Empirical Likelihood

Author

Tang, Rong and Yang, Yun

Subjects
Statistics - Methodology
Abstract

The celebrated Bernstein von-Mises theorem ensures that credible regions from Bayesian posterior are well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or only partly specified, such as in quantile regression, risk minimization based supervised/unsupervised learning and robust estimation. To overcome this difficulty, we propose a new Bayesian inferential approach that substitutes the (misspecified or partly specified) likelihoods with proper exponentially tilted empirical likelihoods plus a regularization term. Our surrogate empirical likelihood is carefully constructed by using the first order optimality condition of the empirical risk minimization as the moment condition. We show that the Bayesian posterior obtained by combining this surrogate empirical likelihood and the prior is asymptotically close to a normal distribution centering at the empirical risk minimizer with covariance matrix taking an appropriate sandwiched form. Consequently, the resulting Bayesian credible regions are automatically calibrated to deliver valid uncertainty quantification. Computationally, the proposed method can be easily implemented by Markov Chain Monte Carlo sampling algorithms. Our numerical results show that the proposed method tends to be more accurate than existing state-of-the-art competitors., Comment: 78 pages, 5 figures

Published
2021

278. AdvDrop: Adversarial Attack to DNNs by Dropping Information

Author

Duan, Ranjie, Chen, Yuefeng, Niu, Dantong, Yang, Yun, Qin, A. K., and He, Yuan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep Neural Networks (DNNs), the ability for recognizing abstract objects (visual objects with lost information) is still a challenge. In this work, we investigate this issue from an adversarial viewpoint: will the performance of DNNs decrease even for the images only losing a little information? Towards this end, we propose a novel adversarial attack, named \textit{AdvDrop}, which crafts adversarial examples by dropping existing information of images. Previously, most adversarial attacks add extra disturbing information on clean images explicitly. Opposite to previous works, our proposed work explores the adversarial robustness of DNN models in a novel perspective by dropping imperceptible details to craft adversarial examples. We demonstrate the effectiveness of \textit{AdvDrop} by extensive experiments, and show that this new type of adversarial examples is more difficult to be defended by current defense systems., Comment: Accepted to ICCV 2021

Published
2021

279. Cost-effective Land Cover Classification for Remote Sensing Images

Author

Li, Dongwei, Wang, Shuliang, He, Qiang, and Yang, Yun

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving the cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.

Published
2021

280. Fungal diversity notes 1611–1716: taxonomic and phylogenetic contributions on fungal genera and species emphasis in south China

Author

Senanayake, Indunil C., Rossi, Walter, Leonardi, Marco, Weir, Alex, McHugh, Mark, Rajeshkumar, Kunhiraman C., Verma, Rajnish K., Karunarathna, Samantha C., Tibpromma, Saowaluck, Ashtekar, Nikhil, Ashtamoorthy, Sreejith K., Raveendran, Sanjay, Kour, Gurmeet, Singh, Aishwarya, De la Peña-Lastra, Saúl, Mateos, Antonio, Kolařík, Miroslav, Antonín, Vladimír, Ševčíková, Hana, Esteve-Raventós, Fernando, Larsson, Ellen, Pancorbo, Fermín, Moreno, Gabriel, Altés, Alberto, Turégano, Yolanda, Du, Tian-Ye, Lu, Li, Li, Qi-Rui, Kang, Ji-Chuan, Gunaseelan, Sugantha, Kezo, Kezhocuyi, Kaliyaperumal, Malarvizhi, Fu, Jizhen, Samarakoon, Milan C., Gafforov, Yusufjon, Teshaboeva, Shakhnoza, Kunjan, Pradeep C., Chamaparambath, Arya, Flakus, Adam, Etayo, Javier, Rodriguez-Flakus, Pamela, Zhurbenko, Mikhail P., de Silva, Nimali I., Tennakoon, Danushka S., Latha, K. P. Deepna, Manimohan, Patinjareveettil, Raj, K. N. Anil, Calabon, Mark S., Ahmadpour, Abdollah, Heidarian, Zeinab, Alavi, Zahra, Alavi, Fatemeh, Ghosta, Youbert, Azizi, Razmig, Luo, Mei, Zhao, Min-Ping, Kularathnage, Nuwan D., Hua, Li, Yang, Yun-Hui, Liao, Chun-Fang, Zhao, Hai-Jun, Lestari, Anis S., Jayasiri, Subashini C., Yu, Feng-Ming, Lei, Lei, Liu, Jian-Wei, Karimi, Omid, Tang, Song-Ming, Sun, Ya-Ru, Wang, Yong, Zeng, Ming, Htet, Zin H., Linaldeddu, Benedetto T., Alves, Artur, Phillips, Alan J. L., Bregant, Carlo, Montecchio, Lucio, De Kesel, André, Hustad, Vincent P., Miller, Andrew N., Fedosova, Anna G., Kučera, Viktor, Raza, Mubashar, Hussain, Muzammil, Chen, Yan-Peng, Thiyagaraja, Vinodhini, Gomdola, Deecksha, Rathnayaka, Achala R., Dissanayake, Asha J., Suwannarach, Nakarin, Hongsanan, Sinang, Maharachchikumbura, Sajeewa S. N., Dissanayake, Lakmali S., Wijayawardene, Nalin N., Phookamsak, Rungtiwa, Lumyong, Saisamorn, Jones, E. B. Gareth, Yapa, Neelamanie, Wanasinghe, Dhanushka N., Xie, Ning, Doilom, Mingkwan, Manawasinghe, Ishara S., Liu, Jian-Kui (Jack), Zhao, Qi, Xu, Biao, Hyde, Kevin D., and Song, Jiage

Published
2023
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