Generative-Discriminative Complementary Learning

Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and can generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data

Development of Chinese Cultural Governance since the Reform and Opening-up

Contemporary Chinese cultural governance is an important part of the national governance system and the modernization of national governance capacity. It must combine international experience and local method together and run through the whole process of cultural system reform. The good governance of culture is the goal pursued by Chinese cultural governance, which displays the management process of maximizing cultural public interest. In the new era, cultural governance reform has experienced governance subject centralized to pluralism, governance space from inside to outside, governance method from ruling by man to the rule of law, governance path from control-oriented government to service-oriented government. To further improve Chinese cultural governance in the new era, relationships between top-level design and grass-root innovation, cultural democracy and cultural concentration, national cultural interests, social cultural interests and cultural interests of the citizens, domestic cultural governance and global culture must be dialectically dealt with. In the government-market-social governance structure, mutual assistance of multiple missions and win-win of multiple subjects can be achieved, thus promoting harmony of multiple interest

Superconductivity in epitaxially grown LaVO3/KTaO3(111) heterostructures

Complex oxide heterointerfaces can host a rich of emergent phenomena, and epitaxial growth is usually at the heart of forming these interfaces. Recently, a strong crystalline-orientation-dependent two-dimensional superconductivity was discovered at interfaces between KTaO3 single-crystal substrates and films of other oxides. Unexpectedly, rare of these oxide films was epitaxially grown. Here, we report the existence of superconductivity in epitaxially grown LaVO3/KTaO3(111) heterostructures, with a superconducting transition temperature of ~0.5 K. Meanwhile, no superconductivity was detected in the (001)- and (110)-orientated LaVO3/KTaO3 heterostructures down to 50 mK. Moreover, we find that for the LaVO3/KTaO3(111) interfaces to be conducting, an oxygen-deficient growth environment and a minimum LaVO3 thickness of ~0.8 nm (~ 2 unit cells) are needed.Comment: 5 figures, plus 6 supplementary figure

Noise-resilient phase estimation with randomized compiling

We develop an error mitigation method for the control-free phase estimation. We prove a theorem that under the first-order correction, the noise channels with only Hermitian Kraus operators do not change the phases of a unitary operator, and therefore, the benign types of noise for phase estimation are identified. By using the randomized compiling protocol, we can convert the generic noise in the phase estimation circuits into stochastic Pauli noise, which satisfies the condition of our theorem. Thus we achieve a noise-resilient phase estimation without any quantum resource overhead. The simulated experiments show that our method can significantly reduce the estimation error of the phases by up to two orders of magnitude. Our method paves the way for the utilization of quantum phase estimation before the advent of fault-tolerant quantum computers.Comment: 5 pages 4 figures, with the appendix; final version for publicatio