Research on Sustainable Development Issue in the Vision of Game Theory

Sustainable development issues belong to external non-economical common land based concerns. From an historical viewpoint, the main responsibility for the current series of global environment problem should be taken on by developed countries. Therefore, on sustainable development issue, international society has formed a principle of “common but different” responsibility-sharing rule. By game theory model, it is found that the reason why the negotiation between developed countries and developing ones reaches a deadlock again and again is short of proper conditions or mechanism to co-operate. There are at least three prerequisites to solve sustainable development issues, none of which has been met so far. Key words: Game theory; Sustainable development; Prerequisite; Innovation Mechanis

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Pakistan-China Relations: CPEC and Beyond

This contribution is based on the proceedings of a session on the same title organized by the Institute of Policy Studies (IPS), Islamabad on August 10, 2017. The highlight of the event, as is recorded in this article, was the exclusive talk of H.E. Sun Weidong, Chinese ambassador to Pakistan while the opening and concluding remarks by Khalid Rahman, Executive President of IPS and Senator Raja Zafar-ul-Haq, Leader of the House in the Senate of Pakistan, respectively, are also part of the article. Pleasantries have been edited for the purpose of brevity and consistency

Pattern Classification Using A Fuzzy Immune Network Model

It is generally believed that one major function of immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model(here, we call it the binary model) based on biological immune response network was proposed in our previous work. However, there are some problems like input and memory in the binary model. In order to improve the binary model, in this paper we propose a fuzzy immune network model. In the proposed fuzzy immune model, we add a normalization B cell layer for normalizing the large-scale antigen information on the base of the binary model. Meanwhile, a fuzzy AND operator(.AND.) and a normalization procedure called complement coding were employed in the proposed fuzzy immune model. Compute simulations illustrate that the proposed fuzzy model not only can improve the problems existing in the binary model but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories. (author abst.