266 research outputs found
补贴作为应对市场失灵的手段:欧盟国家援助政策对中国的启示
This paper discusses the role of subsidies in correcting market failures from the perspective of EU State aid policy. In recent years, one can notice a clear shift towards a 'more economic approach’ and a stronger focus on efficiency by the European Commission. However, this stronger focus on efficiency with regard to a politically sensitive area such as State aid is not self-evident, and also raises the question why other jurisdictions do not have a similar control on market intervention by States (US) or provinces (China). It also raises the question whether controlling the efficiency of government spending should be a task of the EU rathert han Member States. The aim of this paper is therefore to critically assess the changing goals of EU State aid policy, from market integration and equity to efficiency and fiscal discipline. Possible implications for China, in the form of a stricter control on subsidies,are also discussed
Comparative Analysis of Merger Control Policy: lessons from China
__Abstract__
After thirteen years of discussion, on 30 August 2007, the Anti-Monopoly Law of
the People’s Republic of China (‘AML’) was promulgated by the 29th session of
the 10th Standing Committee of China’s National People’s Congress, and this law
came into force on 1 August 2008.
Although China is not the fi rst developing country to adopt a competition
law, there are several reasons which make this AML special. Given China’s
incomparable level of inv
Few-shot Image Generation via Masked Discrimination
Few-shot image generation aims to generate images of high quality and great
diversity with limited data. However, it is difficult for modern GANs to avoid
overfitting when trained on only a few images. The discriminator can easily
remember all the training samples and guide the generator to replicate them,
leading to severe diversity degradation. Several methods have been proposed to
relieve overfitting by adapting GANs pre-trained on large source domains to
target domains with limited real samples. In this work, we present a novel
approach to realize few-shot GAN adaptation via masked discrimination. Random
masks are applied to features extracted by the discriminator from input images.
We aim to encourage the discriminator to judge more diverse images which share
partially common features with training samples as realistic images.
Correspondingly, the generator is guided to generate more diverse images
instead of replicating training samples. In addition, we employ cross-domain
consistency loss for the discriminator to keep relative distances between
samples in its feature space. The discriminator cross-domain consistency loss
serves as another optimization target in addition to adversarial loss and
guides adapted GANs to preserve more information learned from source domains
for higher image quality. The effectiveness of our approach is demonstrated
both qualitatively and quantitatively with higher quality and greater diversity
on a series of few-shot image generation tasks than prior methods
A Two-Dimensional Simulation Model of the Bicoid Gradient in Drosophila
BACKGROUND:Bicoid (Bcd) is a Drosophila morphogenetic protein responsible for patterning the anterior structures in embryos. Recent experimental studies have revealed important insights into the behavior of this morphogen gradient, making it necessary to develop a model that can recapitulate the biological features of the system, including its dynamic and scaling properties. METHODOLOGY/PRINCIPAL FINDINGS:We present a biologically realistic 2-D model of the dynamics of the Bcd gradient in Drosophila embryos. This model is based on equilibrium binding of Bcd molecules to non-specific, low affinity DNA sites throughout the Drosophila genome. It considers both the diffusion media within which the Bcd gradient is formed and the dynamic and other relevant properties of bcd mRNA from which Bcd protein is produced. Our model recapitulates key features of the Bcd protein gradient observed experimentally, including its scaling properties and the stability of its nuclear concentrations during development. Our simulation model also allows us to evaluate the effects of other biological activities on Bcd gradient formation, including the dynamic redistribution of bcd mRNA in early embryos. Our simulation results suggest that, in our model, Bcd protein diffusion is important for the formation of an exponential gradient in embryos. CONCLUSIONS/SIGNIFICANCE:The 2-D model described in this report is a simple and versatile simulation procedure, providing a quantitative evaluation of the Bcd gradient system. Our results suggest an important role of Bcd binding to non-specific, low-affinity DNA sites in proper formation of the Bcd gradient in our model. They demonstrate that highly complex biological systems can be effectively modeled with relatively few parameters
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset
Nowadays, billions of videos are online ready to be viewed and shared. Among
an enormous volume of videos, some popular ones are widely viewed by online
users while the majority attract little attention. Furthermore, within each
video, different segments may attract significantly different numbers of views.
This phenomenon leads to a challenging yet important problem, namely
fine-grained video attractiveness prediction. However, one major obstacle for
such a challenging problem is that no suitable benchmark dataset currently
exists. To this end, we construct the first fine-grained video attractiveness
dataset, which is collected from one of the most popular video websites in the
world. In total, the constructed FVAD consists of 1,019 drama episodes with
780.6 hours covering different categories and a wide variety of video contents.
Apart from the large amount of videos, hundreds of millions of user behaviors
during watching videos are also included, such as "view counts",
"fast-forward", "fast-rewind", and so on, where "view counts" reflects the
video attractiveness while other engagements capture the interactions between
the viewers and videos. First, we demonstrate that video attractiveness and
different engagements present different relationships. Second, FVAD provides us
an opportunity to study the fine-grained video attractiveness prediction
problem. We design different sequential models to perform video attractiveness
prediction by relying solely on video contents. The sequential models exploit
the multimodal relationships between visual and audio components of the video
contents at different levels. Experimental results demonstrate the
effectiveness of our proposed sequential models with different visual and audio
representations, the necessity of incorporating the two modalities, and the
complementary behaviors of the sequential prediction models at different
levels.Comment: Accepted by WWW 2018 The Big Web Trac
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