480 research outputs found
On Blockchain We Cooperate: An Evolutionary Game Perspective
Cooperation is fundamental for human prosperity. Blockchain, as a trust
machine, is a cooperative institution in cyberspace that supports cooperation
through distributed trust with consensus protocols. While studies in computer
science focus on fault tolerance problems with consensus algorithms, economic
research utilizes incentive designs to analyze agent behaviors. To achieve
cooperation on blockchains, emerging interdisciplinary research introduces
rationality and game-theoretical solution concepts to study the equilibrium
outcomes of various consensus protocols. However, existing studies do not
consider the possibility for agents to learn from historical observations.
Therefore, we abstract a general consensus protocol as a dynamic game
environment, apply a solution concept of bounded rationality to model agent
behavior, and resolve the initial conditions for three different stable
equilibria. In our game, agents imitatively learn the global history in an
evolutionary process toward equilibria, for which we evaluate the outcomes from
both computing and economic perspectives in terms of safety, liveness,
validity, and social welfare. Our research contributes to the literature across
disciplines, including distributed consensus in computer science, game theory
in economics on blockchain consensus, evolutionary game theory at the
intersection of biology and economics, bounded rationality at the interplay
between psychology and economics, and cooperative AI with joint insights into
computing and social science. Finally, we discuss that future protocol design
can better achieve the most desired outcomes of our honest stable equilibria by
increasing the reward-punishment ratio and lowering both the cost-punishment
ratio and the pivotality rate
The Double-ITCZ Bias in CMIP3, CMIP5 and CMIP6 Models Based on Annual Mean Precipitation
The doubleāintertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual doubleāITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multiāmodel ensemble mean precipitation errors in the tropics. The notorious doubleāITCZ bias and its big interāmodel spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the doubleāITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its interāmodel spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6
The Double-ITCZ Bias in CMIP3, CMIP5 and CMIP6 Models Based on Annual Mean Precipitation
The doubleāintertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual doubleāITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multiāmodel ensemble mean precipitation errors in the tropics. The notorious doubleāITCZ bias and its big interāmodel spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the doubleāITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its interāmodel spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6
A General Divergence Modeling Strategy for Salient Object Detection
Salient object detection is subjective in nature, which implies that multiple
estimations should be related to the same input image. Most existing salient
object detection models are deterministic following a point to point estimation
learning pipeline, making them incapable of estimating the predictive
distribution. Although latent variable model based stochastic prediction
networks exist to model the prediction variants, the latent space based on the
single clean saliency annotation is less reliable in exploring the subjective
nature of saliency, leading to less effective saliency divergence modeling.
Given multiple saliency annotations, we introduce a general divergence modeling
strategy via random sampling, and apply our strategy to an ensemble based
framework and three latent variable model based solutions to explore the
subjective nature of saliency. Experimental results prove the superior
performance of our general divergence modeling strategy.Comment: Code is available at: https://npucvr.github.io/Divergence_SOD
Quaternion MLP Neural Networks Based on the Maximum Correntropy Criterion
We propose a gradient ascent algorithm for quaternion multilayer perceptron
(MLP) networks based on the cost function of the maximum correntropy criterion
(MCC). In the algorithm, we use the split quaternion activation function based
on the generalized Hamilton-real quaternion gradient. By introducing a new
quaternion operator, we first rewrite the early quaternion single layer
perceptron algorithm. Secondly, we propose a gradient descent algorithm for
quaternion multilayer perceptron based on the cost function of the mean square
error (MSE). Finally, the MSE algorithm is extended to the MCC algorithm.
Simulations show the feasibility of the proposed method
FRACTAL SPACE BASED DIMENSIONLESS ANALYSIS OF THE SURFACE SETTLEMENT INDUCED BY THE SHIELD TUNNELING
The surface settlement during the tunneling process is becoming increasingly difficult to forecast as its surroundings become more and more erratic, and the maximal surface settlement raises risks posed suddenly by various uncertain factors. This paper proposes a novel approach to prediction of the surface settlement and analyzes the stability of tunnel construction. The dimensionless analysis and Buckinghamās Ļ-theorem are adopted for this purpose, and some useful dimensionless quantities are found, which can be used to determine the surface settlementās main properties. In this manner, the paper offers new ways of predicting surface settlement in various cases, and it sheds a new light on the tunnelās design and safety monitoring
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