Social Norm, Costly Punishment and the Evolution to Cooperation

Both laboratory and field evidence suggest that people tend to voluntarily incur costs to punish non-cooperators. While costly punishment typically reduces the average payoff as well as promotes cooperation. Why does the costly punishment evolve? We study the role of punishment in cooperation promotion within a two-level evolution framework of individual strategies and social norms. In a population with certain social norm, players update their strategies according to the payoff differences among different strategies. In a longer horizon, the evolution of social norm may be driven by the average payoffs of all members of the society. Norms differ in whether they allow or do not allow for the punishment action as part of strategies, and, for the former, they further differ in whether they encourage or do not encourage the punishment action. The strategy dynamics are articulated under different social norms. It is found that costly punishment does contribute to the evolution toward cooperation. Not only does the attraction basin of cooperative evolutionary stable state (CESS) become larger, but also the convergence speed to CESS is faster. These two properties are further enhanced if the punishment action is encouraged by the social norm. This model can be used to explain the widespread existence of costly punishment in human society.social norm; costly punishment; cooperative evolutionary stable state; attraction basin; convergence speed

Non-Surgical Treatment Methodologies and Prevention for Malignant Melanoma

Melanocytes in the skin and other organs generate the tumor known as malignant melanoma (MM). It has a high degree of malignancy, a deprived prognosis, and a propensity for local recurrence and distant metastasis. Although there have been tremendous advancements in MM management choices over the past ten years, there are still a dearth of clinically viable therapy alternatives and no internationally accepted treatment standard. The prognosis of MM patients has recently improved thanks to the development of immunotherapy and targeted therapy. As a result, this article examines the most recent findings from studies on the non-surgical treatment methodologies for MM and its preventive measures.Keywords: Malignant melanoma; Treatment therapies; Combined therapies; Prevention 

A Review and Comparison of AI Enhanced Side Channel Analysis

Side Channel Analysis (SCA) presents a clear threat to privacy and security in modern computing systems. The vast majority of communications are secured through cryptographic algorithms. These algorithms are often provably-secure from a cryptographical perspective, but their implementation on real hardware introduces vulnerabilities. Adversaries can exploit these vulnerabilities to conduct SCA and recover confidential information, such as secret keys or internal states. The threat of SCA has greatly increased as machine learning, and in particular deep learning, enhanced attacks become more common. In this work, we will examine the latest state-of-the-art deep learning techniques for side channel analysis, the theory behind them, and how they are conducted. Our focus will be on profiling attacks using deep learning techniques, but we will also examine some new and emerging methodologies enhanced by deep learning techniques, such as non-profiled attacks, artificial trace generation, and others. Finally, different deep learning enhanced SCA schemes attempted against the ANSSI SCA Database (ASCAD) and their relative performance will be evaluated and compared. This will lead to new research directions to secure cryptographic implementations against the latest SCA attacks.Comment: This paper has been accepted by ACM Journal on Emerging Technologies in Computing Systems (JETC

Dynamic Regimes of a Multi-agent Stock Market Model

This paper presents a stochastic multi-agent model of stock market. The market dynamics include switches between chartists and fundamentalists and switches in the prevailing opinions (optimistic or pessimistic) among chartists. A nonlinear dynamical system is derived to depict the underlying mechanisms of market evolvement. Under different settings of parameters representing traders' mimetic contagion propensity, price chasing propensity and strategy switching propensity, the system exhibits four kinds of dynamic regimes: fundamental equilibrium, non-fundamental equilibrium, periodicity and chaos

Covering the Cover

Background/Aims: Endoscopic submucosal dissection has been widely accepted. At present, the number of antiplatelet (APT) users has been growing. Moreover, because of high risks of thromboembolism, some patients need to continuously receive APT agents. The relationship between hemorrhage and continuous therapy with low-dose aspirin (LDA) remains controversial. Materials and Methods: A systematic search was conducted; studies were screened out- if data of no-anticoagulant/APT drugs use and interrupted and continued-LDA use were reported separately. The Newcastle-scale was chosen to assess the quality of the included studies. Review Manager 5.2 was used for quality assessment statistical analysis, and the odd ratio (OR) and 95% confidence interval (CI) were calculated. Results: Pooled data suggested a significantly higher bleeding ratio in the LDA-continued group compared to both the LDA-interrupted group (OR=2.05, 95% CI=1.05-3.99) and no-anticoagulant/APT group (OR=2.89, 95% CI=1.86-4.47). However, the LDA-interrupted group did not differ significantly from the no-anticoagulant/APT group. The en bloc resection rates of the LDA-continued group versus the LDA-interrupted group, the LDAcontinued group versus no-anticoagulant/APT group, and the LDA-interrupted group versus the no-anticoagulant/APT group were similar (OR=0.82, 95% CI=0.21-3.24, p=0.78; OR=0.80, 95% CI=0.24-2.65, p=0.71; OR=1.41, 95% CI=0.38-5.24, p=0.60, respectively). Conclusion: There is an extremely high ratio of bleeding in the LDA-continued group compared to both the LDA-interrupted group and no-anticoagulant/APT group. All groups had similar ratios of en bloc resection

Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results