931 research outputs found
Discrepancy and Synergy between China and the International Criminal Court
International legal scholarship pays much attention to normative interpretations of China’s stance toward international criminal justice, which contributes little to the potential synergy between the two. This article develops the current analytical framework in two ways: first by outlining the rationale behind China’s conventional critique of the ICC, namely concerning supranational jurisdiction, judicial complementarity and situations in Africa, that results in the discrepancy; secondly, it examines the shift in China’s diplomatic strategy and domestic judicial reforms, and the expanding presence in Africa that bring about an alternative approach. This article then concludes with four factors that are likely to lead to an optimal relationship between China and the ICC
Exclusive Decays to Charmonium and a Light Meson at Next-to-Leading Order Accuracy
In this paper the next-to-leading order (NLO) corrections to meson
exclusive decays to S-wave charmonia and light pseudoscalar or vector mesons,
i.e. , , , and , are performed within non-relativistic (NR)
QCD approach. The non-factorizable contribution is included, which is absent in
traditional naive factorization (NF). And the theoretical uncertainties for
their branching ratios are reduced compared with that of direct tree level
calculation. Numerical results show that NLO QCD corrections markedly enhance
the branching ratio with a K factor of 1.75 for and 1.31 for . In order to
investigate the asymptotic behavior, the analytic form is obtained in the heavy
quark limit, i.e. . We note that annihilation topologies
contribute trivia in this limit, and the corrections at leading order in expansion come from form factors and hard spectator interactions. At
last, some related phenomenologies are also discussed.Comment: 20 pages, 7 figures and 5 table
The coevolution of overconfidence and bluffing in the resource competition game
Resources are often limited, therefore it is essential how convincingly competitors present their claims
for them. Beside a player’s natural capacity, here overconfidence and bluffing may also play a decisive
role and influence how to share a restricted reward. While bluff provides clear, but risky advantage,
overconfidence, as a form of self-deception, could be harmful to its user. Still, it is a long-standing
puzzle why these potentially damaging biases are maintained and evolving to a high level in the
human society. Within the framework of evolutionary game theory, we present a simple version of
resource competition game in which the coevolution of overconfidence and bluffing is fundamental,
which is capable to explain their prevalence in structured populations. Interestingly, bluffing seems
apt to evolve to higher level than corresponding overconfidence and in general the former is less
resistant to punishment than the latter. Moreover, topological feature of the social network plays an
intricate role in the spreading of overconfidence and bluffing. While the heterogeneity of interactions
facilitates bluffing, it also increases efficiency of adequate punishment against overconfident behavior.
Furthermore, increasing the degree of homogeneous networks can trigger similar effect. We also
observed that having high real capability may accommodate both bluffing ability and overconfidence
simultaneously
PCGAN: Partition-Controlled Human Image Generation
Human image generation is a very challenging task since it is affected by
many factors. Many human image generation methods focus on generating human
images conditioned on a given pose, while the generated backgrounds are often
blurred.In this paper,we propose a novel Partition-Controlled GAN to generate
human images according to target pose and background. Firstly, human poses in
the given images are extracted, and foreground/background are partitioned for
further use. Secondly, we extract and fuse appearance features, pose features
and background features to generate the desired images. Experiments on
Market-1501 and DeepFashion datasets show that our model not only generates
realistic human images but also produce the human pose and background as we
want. Extensive experiments on COCO and LIP datasets indicate the potential of
our method.Comment: AAAI 2019 versio
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