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On the Estimation of Shannon Entropy [PDF]

open access: yesJournal of Statistical Research of Iran, 2015
Shannon entropy is increasingly used in many applications. In this article, an estimator of the entropy of a continuous random variable is proposed. Consistency and scale invariance of variance and mean squared error of the proposed estimator is proved and then comparisons are made with Vasicek’s (1976), van Es (1992), Ebrahimi et al. (1994) and Correa
openaire   +1 more source

Compact Tabletop Magnetic Resonance Elastography for Mapping Soft Tissue Viscoelasticity

open access: yesAdvanced Science, EarlyView.
This work introduces a compact, low‐cost tabletop magnetic resonance elastography platform for high‐resolution viscoelastic mapping in soft‐tissue specimens. Using this method in human colorectal liver metastases, we demonstrate fully automated biomechanical profiling of treatment response and show that heterogeneity‐based metrics outperform ...
Weijie Zhao   +19 more
wiley   +1 more source

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model.

open access: yesPLoS ONE, 2015
Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus ...
Anne Chao   +5 more
doaj   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez   +10 more
wiley   +1 more source

Quantifying Daseinisation Using Shannon Entropy

open access: yesWSEAS TRANSACTIONS ON SYSTEMS, 2020
Topos formalism for quantum mechanics is interpreted in a broader, information retrieval, perspective. Contexts, its basic components, are treated as sources of information. Their interplay, called daseinisation, defined in purely logical terms, is reformulated in terms of two relations: exclusion and preclusion of queries.
openaire   +2 more sources

Coping With Production Risk: Effects of Sown Plant Diversity on the Attractiveness of Crop Insurance in Grasslands

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT Increased frequency of extreme weather events, particularly droughts, threatens grassland farming by destabilizing yields and farms' economic viability. We examine, theoretically and through numerical simulations, how sown plant diversity (natural insurance) influences the attractiveness of indemnity and drought index insurance (formal ...
Nicolas Alou   +3 more
wiley   +1 more source

A note on Shannon entropy

open access: yesCoRR, 2012
We present a somewhat different way of looking on Shannon entropy. This leads to an axiomatisation of Shannon entropy that is essentially equivalent to that of Fadeev. In particular we give a new proof of Fadeev theorem.
openaire   +2 more sources

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah   +3 more
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang   +4 more
wiley   +1 more source

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