A Maximum Entropy Method for the Prediction of Size Distributions

Metzig, Cornelia; Colijn, Caroline

doi:10.3390/e22030312

Cited by 5 publications

(7 citation statements)

References 43 publications

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“…(2) MEP mixes hyperchaotic system with improved AES algorithm. First, the MEP function reads the data set after slicing and reads it in the way of key value pair [31]. <zej W in , value W in > represents the input key value…”

Section: Algorithm Designmentioning

confidence: 99%

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Zhong¹,

Zhang

Zhao

2022

Wireless Communications and Mobile Computing

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Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.

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Section: Algorithm Designmentioning

confidence: 99%

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Zhong¹,

Zhang

Zhao

2022

Wireless Communications and Mobile Computing

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“…remembering that β i ≡ β i (λ i ) is defined by (46). To show that function ( 54) is a SPM we show that…”

Section: B the Expected Value Of The Replica Field Network Partition ...mentioning

confidence: 99%

“…For instance, using the heat bath analogy, where the energy states are related to the eigenvalues of a matrix representation of network structure, particles, which are in thermal equilibrium with the heat bath, begin to populate these energy states. Within this thermalization process, the energy states can be described by Maxwell-Boltzmann [40,46], Bose-Einstein [27,40], and Fermi-Dirac [40,[47][48][49][50] occupation statistics. On the other hand, in [35] the authors show that the partition function can be computed from the matrix characteristic polynomial.…”

Section: Introductionmentioning

confidence: 99%

Systemic Performance Measures from Distributional Zeta-Function

Rodríguez-Camargo,

Urquijo-Rodríguez,

Mojica-Nava

2020

Preprint

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We propose the use of the Distributional Zeta-Function (DZF) for constructing a new set of Systemic Performance Measures (SPM). SPM have been proposed to investigate network synthesis problems such as the growing of linear consensus networks. The adoption of the DZF has shown interesting physical consequences that in the usual replica method are still unclarified, i.e., the connection between the spontaneous symmetry breaking mechanism and the structure of the replica space in the disordered model. We relate topology of the network and the partition function present in the DZF by using the spectral and the Hamiltonian structure of the system. The studied objects are the generalized partition funcion, the DZF, the Expected value of the replica partition function, and the quenched free energy of a field network. We show that with these objects we need few operations to increase the percentage of performance enhancement of a network. Furthermore, we evalue the location of the optimal added links for each new SPM and calculate the performance improvement of the new network for each new SPM via the spectral zeta function, H2-norm, and the communicability between nodes. We present the advantages of this new set of SPM in the network synthesis and we propose other methods for using the DZF to explore some issues such as disorder, critical phenomena, finite-temperature, and finite-size effects on networks. Relevance of the results are discussed.

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“…Trees contain both branch lengths and information in the form of the tree topology or shape. Phylogenetic trees have been used in infectious disease to estimate the basic reproduction number [3], parameters of transmission models [4], aspects of underlying contact networks [5][6][7][8] and in densely sampled datasets even person-to-person transmission events and timing [9][10][11][12]. It is therefore natural to hypothesize that phylogenetic tree structures and branching patterns contain information about short-term growth and fitness.…”

Section: Introductionmentioning

confidence: 99%

“…Tree information is central in some predictive models for short-term influenza virus evolution and models of fitness [1,2]. However, the mapping between the phylogenetic tree structure and interpretable biological information can be subtle, [7,8,13,14] and trees do not directly reveal the short-term evolutionary trajectories of groups of taxa.…”

Section: Introductionmentioning

confidence: 99%

Predicting the short-term success of human influenza virus variants with machine learning

2020

Self Cite

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Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body’s immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines’ benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71–0.85 and a classifier ‘area under the curve’ 0.75–0.9.

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A Maximum Entropy Method for the Prediction of Size Distributions

Cited by 5 publications

References 43 publications

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Systemic Performance Measures from Distributional Zeta-Function

Predicting the short-term success of human influenza virus variants with machine learning

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