We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations ind...
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We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations indicate that, compared to the model with only one update rule, the the role of update dynamics should be paid more attention in cooperation behavior exhibits the richer phenomena, and the evolutionary game theory. Meanwhile, we also observe that the introduction of Moran rule, which needs to consider all neighbor＇s information, can markedly promote the aggregate cooperation level, that is, randomly selecting the neighbor proportional to its payoff to imitate will facilitate the cooperation among agents. Current results will contribute to further understand the cooperation dynamics and evolutionary behaviors within many biological, economic and social systems.
Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most exist...
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Recent research results indicate that individual awareness can play an important influence on epidemic spreading in networks. By local stability analysis, a significant conclusion is that the embedded awareness in an ...
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Recent research results indicate that individual awareness can play an important influence on epidemic spreading in networks. By local stability analysis, a significant conclusion is that the embedded awareness in an epidemic network can increase its epidemic threshold. In this paper, by using limit theory and dynamical system theory, we further give global stability analysis of a susceptible-infected-susceptible （SIS） epidemic model on networks with awareness. Results show that the obtained epidemic threshold is also a global stability condition for its endemic equilibrium, which implies the embedded awareness can enhance the epidemic threshold globally. Some numerical examples are presented to verify the theoretical results.
In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may mak...
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In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.
Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power...
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Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple Data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.
Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of t...
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Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.
It can be observed from looking backward that processor architecture is improved through spirally shifting from simple to complex and from complex to simple. Nowadays we are facing another shifting from complex to sim...
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It can be observed from looking backward that processor architecture is improved through spirally shifting from simple to complex and from complex to simple. Nowadays we are facing another shifting from complex to simple, and new innovative architecture will emerge to utilize the continuously increasing transistor budgets. The growing importance of wire delays, changing workloads, power consumption, and design/verification complexity will drive the forthcoming era of Chip Multiprocessors （CMPs）. Furthermore, typical CMP projects both from industries and from academics are investigated. Through going into depths for some primary theoretical and implementation problems of CMPs, the great challenges and opportunities to future CMPs are presented and discussed. Finally, the Godson series microprocessors designed in China are introduced.
Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, ...
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Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, low cost, and energy efficiency, at the expense of high write latency, high write power consumption, and limited write endurance. NVMMs have become a competitive alternative of Dynamic Random Access Memory (DRAM), and will fundamentally change the landscape of memory systems. They bring many research opportunities as well as challenges on system architectural designs, memory management in operating systems (OSes), and programming models for hybrid memory systems. In this article, we revisit the landscape of emerging NVMM technologies, and then survey the state-of-the-art studies of NVMM technologies. We classify those studies with a taxonomy according to different dimensions such as memory architectures, data persistence, performance improvement, energy saving, and wear leveling. Second, to demonstrate the best practices in building NVMM systems, we introduce our recent work of hybrid memory system designs from the dimensions of architectures, systems, and applications. At last, we present our vision of future research directions of NVMMs and shed some light on design challenges and opportunities.
Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accura...
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Current timestamp-supervised temporal action segmentation (TS-TAS) methods typically follow a two-phase pipeline: initializing the model with timestamp labels and refining it with pseudo-labels. However, limited by th...
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