18 research outputs found
A Kind of Risk-Sensitive Group Decision-Making Based on MDP
Abstract. One-switch utility function is used to describe how the risk attitude of a decision maker changes with his wealth level. In this paper additive decision rule is used for the aggregation of decision member's utility which is represented by one-switch utility function. Based on Markov decision processes (MDP) and group utility, a dynamic, multi-stages and risk sensitive group decision model is proposed. The proposed model augments the state of MDP with wealth level, so the policy of the model is defined as an action executed in a state and a wealth level interval. A backward-induction algorithm is given to solve the optimal policy for the model. Numerical examples show that personal risk attitude has a great influence on group decision-making when personal risk attitudes of members are different, while the weights of members play a critical role when personal risk attitudes of members are similar
Identifying and decoupling many-body interactions in spin ensembles in diamond
We simulate the dynamics of varying density quasi-two-dimensional spin
ensembles in solid-state systems, focusing on the nitrogen-vacancy centers in
diamond. We consider the effects of various control sequences on the averaged
dynamics of large ensembles of spins, under a realistic "spin-bath"
environment. We reveal that spin locking is efficient for decoupling spins
initialized along the driving axis, both from coherent dipolar interactions and
from the external spin-bath environment, when the driving is two orders of
magnitude stronger than the relevant coupling energies. Since the application
of standard pulsed dynamical decoupling sequences leads to strong decoupling
from the environment, while other specialized pulse sequences can decouple
coherent dipolar interactions, such sequences can be used to identify the
dominant interaction type. Moreover, a proper combination of pulsed decoupling
sequences could lead to the suppression of both interaction types, allowing
additional spin manipulations. Finally, we consider the effect of finite-width
pulses on these control protocols and identify improved decoupling efficiency
with increased pulse duration, resulting from the interplay of dephasing and
coherent dynamics
A knowledge graph empowered online learning framework for access control decision-making
Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status
Risk-Sensitive Multiagent Decision-Theoretic Planning Based on MDP and One-Switch Utility Functions
In high stakes situations decision-makers are often risk-averse and decision-making processes often take place in group settings. This paper studies multiagent decision-theoretic planning under Markov decision processes (MDPs) framework with considering the change of agent’s risk attitude as his wealth level varies. Based on one-switch utility function that describes agent’s risk attitude change with his wealth level, we give the additive and multiplicative aggregation models of group utility and adopt maximizing expected group utility as planning objective. When the wealth level approaches infinity, the characteristics of optimal policy are analyzed for the additive and multiplicative aggregation model, respectively. Then a backward-induction method is proposed to divide the wealth level interval from negative infinity to initial wealth level into subintervals and determine the optimal policy in states and subintervals. The proposed method is illustrated by numerical examples and the influences of agent’s risk aversion parameters and weights on group decision-making are also analyzed
A Kind of Risk-Sensitive Group Decision-Making Based on MDP
Abstract. One-switch utility function is used to describe how the risk attitude of a decision maker changes with his wealth level. In this paper additive decision rule is used for the aggregation of decision member's utility which is represented by one-switch utility function. Based on Markov decision processes (MDP) and group utility, a dynamic, multi-stages and risk sensitive group decision model is proposed. The proposed model augments the state of MDP with wealth level, so the policy of the model is defined as an action executed in a state and a wealth level interval. A backward-induction algorithm is given to solve the optimal policy for the model. Numerical examples show that personal risk attitude has a great influence on group decision-making when personal risk attitudes of members are different, while the weights of members play a critical role when personal risk attitudes of members are similar
Knowledge-driven cybersecurity intelligence: software vulnerability co-exploitation behaviour discovery
Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning
Transcriptome analysis of wheat grain using RNA-Seq
With the increase in consumer demand, wheat grain quality improvement has become a focus in China and worldwide. Transcriptome analysis is a powerful approach to research grain traits and elucidate their genetic regulation. In this study, two cDNA libraries from the developing grain and leaf-stem components of bread wheat cultivar, Nongda211, were sequenced using Roche/454 technology. There were 1061274 and 1516564 clean reads generated from grain and leaf-stem, respectively. A total of 61393 high-quality unigenes were obtained with an average length of 1456 bp after de novo assembly. The analysis of the 61393 unigenes involved in the biological processes of the grain showed that there were 7355 differentially expressed genes upregulated in the grain library. Gene ontology enrichment and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that many transcription products and transcription factors associated with carbohydrate and protein metabolism were abundantly expressed in the grain. These results contribute to excavate genes associated with wheat quality and further study how they interact