scholarly journals Space-Optimal Population Protocols for Uniform Bipartition Under Global Fairness

2019 ◽  
Vol E102.D (3) ◽  
pp. 454-463
Author(s):  
Hiroto YASUMI ◽  
Fukuhito OOSHITA ◽  
Ken'ichi YAMAGUCHI ◽  
Michiko INOUE
Keyword(s):  
Population Protocols ◽  
Optimal Population ◽  
Global Fairness

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Attachment and Axiology

2018 ◽  
Author(s):  
Samuel Scheffler
Keyword(s):  
Future Generations ◽  
States Of Affairs ◽  
Relative Value ◽  
Optimal Population ◽  
Population Axiology

Many discussions of future generations attempt to identify a satisfactory population axiology: a principle that would enable us to assess the relative value of total states of affairs that differ in population-related respects. Such an axiology would in turn supply the basis for a principle of beneficence, which would spell out our responsibilities for promoting optimal population outcomes. By contrast, the approach defended in this book is predominantly attachment-based. The reasons for caring about the fate of future generations discussed in previous chapters all depend on our existing values and evaluative attachments and on our conservative disposition to preserve and sustain the things that we value. This chapter appeals to the nature of valuing to clarify these forms of dependence, and it explores the contrast between the axiological and attachment-based approaches.

scholarly journals Towards efficient verification of population protocols

2021 ◽  
Author(s):  
Michael Blondin ◽  
Javier Esparza ◽  
Stefan Jaax ◽  
Philipp J. Meyer
Keyword(s):  
Linear Constraints ◽  
Computational Power ◽  
High Complexity ◽  
Reachability Problem ◽  
Population Protocols ◽  
Finite State ◽  
State Agents ◽  
Efficient Verification ◽  
Primitive Recursive ◽  
Very High

AbstractPopulation protocols are a well established model of computation by anonymous, identical finite-state agents. A protocol is well-specified if from every initial configuration, all fair executions of the protocol reach a common consensus. The central verification question for population protocols is the well-specification problem: deciding if a given protocol is well-specified. Esparza et al. have recently shown that this problem is decidable, but with very high complexity: it is at least as hard as the Petri net reachability problem, which is -hard, and for which only algorithms of non-primitive recursive complexity are currently known. In this paper we introduce the class $${ WS}^3$$ WS 3 of well-specified strongly-silent protocols and we prove that it is suitable for automatic verification. More precisely, we show that $${ WS}^3$$ WS 3 has the same computational power as general well-specified protocols, and captures standard protocols from the literature. Moreover, we show that the membership and correctness problems for $${ WS}^3$$ WS 3 reduce to solving boolean combinations of linear constraints over $${\mathbb {N}}$$ N . This allowed us to develop the first software able to automatically prove correctness for all of the infinitely many possible inputs.

scholarly journals Time-Optimal Self-Stabilizing Leader Election in Population Protocols

2021 ◽  
Author(s):  
Janna Burman ◽  
Ho-Lin Chen ◽  
Hsueh-Ping Chen ◽  
David Doty ◽  
Thomas Nowak ◽  
...  
Keyword(s):  
Leader Election ◽  
Population Protocols ◽  
Time Optimal

Enhanced Phase Clocks, Population Protocols, and Fast Space Optimal Leader Election

2021 ◽  
Vol 68 (1) ◽  
pp. 1-21
Author(s):  
Leszek Gąsieniec ◽  
Grzegorz Stachowiak
Keyword(s):  
Leader Election ◽  
Population Protocols

Heterogeneous fairness algorithm based on federated learning in intelligent transportation system

2021 ◽  
pp. 1-9
Author(s):  
Yue Jiang ◽  
Gaochao Xu ◽  
Zhiyi Fang ◽  
Shinan Song ◽  
Bingbing Li
Keyword(s):  
Intelligent Transportation System ◽  
Influence Factor ◽  
Learning Algorithm ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Massive Data ◽  
Infrared Sensors ◽  
Distributed Sensors ◽  
Average Accuracy ◽  
Global Fairness

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.

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