Topical Collection "Maximum Entropy and Its Applications"
A topical collection in Entropy (ISSN 1099-4300).
Topical Collection Information
The field of entropy-related research has been particularly fruitful in the past few decades and continues to produce important results in a range of scientific areas, including statistical physics, quantum communications, environmental systems, and natural language processing and network analysis. Contributions to this Collection are welcome from both the theoretical and applied perspectives of entropy, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information. Papers on foundational issues involving the theory of maximum entropy are also welcome.
Dr. Dawn E. Holmes
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- Maximum entropy
- Bayesian maximum entropy
- Relative entropy
- Entropy loss
- Information theory
- Estimating missing information
Published Papers (2 papers)
About the Definition of the Local Equilibrium Lattice Temperature in Suspended Monolayer Graphene
Viewed by 522
The definition of temperature in non-equilibrium situations is among the most controversial questions in thermodynamics and statistical physics. In this paper, by considering two numerical experiments simulating charge and phonon transport in graphene, two different definitions of local lattice temperature are investigated: one
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The definition of temperature in non-equilibrium situations is among the most controversial questions in thermodynamics and statistical physics. In this paper, by considering two numerical experiments simulating charge and phonon transport in graphene, two different definitions of local lattice temperature are investigated: one based on the properties of the phonon–phonon collision operator, and the other based on energy Lagrange multipliers. The results indicate that the first one can be interpreted as a measure of how fast the system is trying to approach the local equilibrium, while the second one as the local equilibrium lattice temperature. We also provide the explicit expression of the macroscopic entropy density for the system of phonons, by which we theoretically explain the approach of the system toward equilibrium and characterize the nature of the equilibria, in the spatially homogeneous case.
A Bootstrap Framework for Aggregating within and between Feature Selection Methods
Cited by 2
| Viewed by 616
In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection
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In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.