On the Systematic Constructions of Rotation Symmetric Bent Functions with Any Possible Algebraic Degrees

In the literature, few constructions of $n$-variable rotation symmetric bent functions have been presented, which either have restriction on $n$ or have algebraic degree no more than $4$. In this paper, for any even integer $n=2m\ge2$, a first systemic construction of $n$-variable rotation symmetric bent functions, with any possible algebraic degrees ranging from $2$ to $m$, is proposed

The Co-Evolution of Global Legitimation and Technology Upgrading: The Case of Huawei

This study explores the underlying relationship between acquisition of global legitimacy and the search for technology upgrading by Chinese multinational enterprises (MNEs). Using Huawei’s investment in Russia, Kenya, the United Kingdom and Canada as an in-depth case study, we observe that through corporate social responsibility (CSR) activities in foreign markets and engaging with local community, Chinese MNEs can acquire global legitimacy and gradually catch up with industry leaders. However, the process of global legitimation and innovation continues to evolve. We find that, together with engaging in CSR activities, acquisition of sophisticated knowledge and creation of innovation bring more legitimacy challenges to these firms. Thus, we suggest that Chinese MNEs’ global legitimation and innovation processes are closely coupled and mutually influential, resulting in co-evolution

What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

Various types of Multi-Agent Reinforcement Learning (MARL) methods have been developed, assuming that agents' policies are based on true states. Recent works have improved the robustness of MARL under uncertainties from the reward, transition probability, or other partners' policies. However, in real-world multi-agent systems, state estimations may be perturbed by sensor measurement noise or even adversaries. Agents' policies trained with only true state information will deviate from optimal solutions when facing adversarial state perturbations during execution. MARL under adversarial state perturbations has limited study. Hence, in this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to study the fundamental properties of MARL under state uncertainties. We prove that the optimal agent policy and the robust Nash equilibrium do not always exist for an SAMG. Instead, we define the solution concept, robust agent policy, of the proposed SAMG under adversarial state perturbations, where agents want to maximize the worst-case expected state value. We then design a gradient descent ascent-based robust MARL algorithm to learn the robust policies for the MARL agents. Our experiments show that adversarial state perturbations decrease agents' rewards for several baselines from the existing literature, while our algorithm outperforms baselines with state perturbations and significantly improves the robustness of the MARL policies under state uncertainties

Uncertainty Quantification of Collaborative Detection for Self-Driving

Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double-M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double-M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4X improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github.io/double-m-quantification.Comment: 6 pages, 3 figure

On constructions of weightwise perfectly balanced Boolean functions

International audienc

On Correlation Immune Boolean Functions With Minimum Hamming Weight Power of 2

International audienc

A construction method of balanced rotation symmetric Boolean functions on arbitrary even number of variables with optimal algebraic immunity

International audienc

Concrete constructions of weightwise perfectly balanced (2-rotation symmetric) functions with optimal algebraic immunity and high weightwise nonlinearity

International audienc

Concrete constructions of weightwise perfectly balanced (2-rotation symmetric) functions with optimal algebraic immunity and high weightwise nonlinearity

International audienc