Combining Static Partitioning with Dynamic Distribution of Threads

In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low cover rate. The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable to attack. We design a dynamic distribution mechanism based on the random sampling method. This mechanism enables the probability distribution of passwords to be dynamically adjusted and tend toward uniform distribution strictly during the generation process. We apply the dynamic distribution mechanism to the Markov model and propose a dynamic Markov model. Through comparative experiments on the RockYou dataset, we set the optimal adjustment degree α. Compared with the Markov model without the dynamic distribution mechanism, the dynamic Markov model reduced the repetition rate from 75.88% to 66.50% and increased the cover rate from 37.65% to 43.49%. In addition, the dynamic Markov model had the highest cover rate for high-probability passwords. Finally, the model avoided the lack of a deterministic algorithm, and when it was run five times, it reached almost the same cover rate as OMEN.

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Dynamic distribution of endoplasmic reticulum in hippocampal neuron dendritic spines

European Journal of Neuroscience ◽

10.1111/j.1460-9568.2005.04342.x ◽

2005 ◽

Vol 22 (7) ◽

pp. 1793-1798 ◽

Cited By ~ 48

Author(s):

Håkan Toresson ◽

Seth G. N. Grant

Keyword(s):

Endoplasmic Reticulum ◽

Dendritic Spines ◽

Hippocampal Neuron ◽

Dynamic Distribution

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Dynamic distribution of nasal microbial community in yaks (Bos grunniens) at different ages

Tropical Animal Health and Production ◽

10.1007/s11250-021-02996-6 ◽

2021 ◽

Vol 53 (6) ◽

Author(s):

Zhigang Liu ◽

Qingsong Sun ◽

Zhonghua Su ◽

Qudrat Ullah ◽

Weixia Yang ◽

...

Keyword(s):

Microbial Community ◽

Dynamic Distribution ◽

Bos Grunniens ◽

Different Ages

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PI(4,5)P2 Clustering and Its Impact on Biological Functions

Annual Review of Biochemistry ◽

10.1146/annurev-biochem-070920-094827 ◽

2021 ◽

Vol 90 (1) ◽

Author(s):

Yi Wen ◽

Volker M. Vogt ◽

Gerald W. Feigenson

Keyword(s):

Plasma Membrane ◽

Cell Biology ◽

Cellular Proteins ◽

Annual Review ◽

Publication Date ◽

Biological Functions ◽

Cellular Functions ◽

Dynamic Distribution ◽

Multivalent Cation ◽

Lipid Environment

Located at the inner leaflet of the plasma membrane, phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] comprises only 1–2 mol% of total PM lipids. With its synthesis and turnover both spatially and temporally regulated, PI(4,5)P2 recruits and interacts with hundreds of cellular proteins to support a broad spectrum of cellular functions. Several factors contribute to the versatile and dynamic distribution of PI(4,5)P2 in membranes. Physiological multivalent cations such as Ca2+ and Mg2+ can bridge between PI(4,5)P2 headgroups, forming nanoscopic PI(4,5)P2–cation clusters. The distinct lipid environment surrounding PI(4,5)P2 affects the degree of PI(4,5)P2 clustering. In addition, diverse cellular proteins interacting with PI(4,5)P2 can further regulate PI(4,5)P2 lateral distribution and accessibility. This review summarizes the current understanding of PI(4,5)P2 behavior in both cells and model membranes, with emphasis on both multivalent cation– and protein-induced PI(4,5)P2 clustering. Understanding the nature of spatially separated pools of PI(4,5)P2 is fundamental to cell biology. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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