71 research outputs found
A Time-Varying Complex Dynamical Network Model And Its Controlled Synchronization Criteria
Today, complex networks have attracted increasing attention from various
fields of science and engineering. It has been demonstrated that many complex
networks display various synchronization phenomena. In this paper, we introduce
a time-varying complex dynamical network model. We then further investigate its
synchronization phenomenon and prove several network synchronization theorems.
Especially, we show that synchronization of such a time-varying dynamical
network is completely determined by the inner-coupling matrix, and the
eigenvalues and the corresponding eigenvectors of the coupling configuration
matrix of the network.Comment: 13 page
Network analysis of chaotic dynamics in fixed-precision digital domain
When implemented in the digital domain with time, space and value discretized
in the binary form, many good dynamical properties of chaotic systems in
continuous domain may be degraded or even diminish. To measure the dynamic
complexity of a digital chaotic system, the dynamics can be transformed to the
form of a state-mapping network. Then, the parameters of the network are
verified by some typical dynamical metrics of the original chaotic system in
infinite precision, such as Lyapunov exponent and entropy. This article reviews
some representative works on the network-based analysis of digital chaotic
dynamics and presents a general framework for such analysis, unveiling some
intrinsic relationships between digital chaos and complex networks. As an
example for discussion, the dynamics of a state-mapping network of the Logistic
map in a fixed-precision computer is analyzed and discussed.Comment: 5 pages, 9 figure
Random Asynchronous Iterations in Distributed Coordination Algorithms
Distributed coordination algorithms (DCA) carry out information processing
processes among a group of networked agents without centralized information
fusion. Though it is well known that DCA characterized by an SIA (stochastic,
indecomposable, aperiodic) matrix generate consensus asymptotically via
synchronous iterations, the dynamics of DCA with asynchronous iterations have
not been studied extensively, especially when viewed as stochastic processes.
This paper aims to show that for any given irreducible stochastic matrix, even
non-SIA, the corresponding DCA lead to consensus successfully via random
asynchronous iterations under a wide range of conditions on the transition
probability. Particularly, the transition probability is neither required to be
independent and identically distributed, nor characterized by a Markov chain
High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops
Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion
Several models of flocking have been promoted based on simulations with
qualitatively naturalistic behavior. In this paper we provide the first direct
application of computational modeling methods to infer flocking behavior from
experimental field data. We show that this approach is able to infer general
rules for interaction, or lack of interaction, among members of a flock or,
more generally, any community. Using experimental field measurements of homing
pigeons in flight we demonstrate the existence of a basic distance dependent
attraction/repulsion relationship and show that this rule is sufficient to
explain collective behavior observed in nature. Positional data of individuals
over time are used as input data to a computational algorithm capable of
building complex nonlinear functions that can represent the system behavior.
Topological nearest neighbor interactions are considered to characterize the
components within this model. The efficacy of this method is demonstrated with
simulated noisy data generated from the classical (two dimensional) Vicsek
model. When applied to experimental data from homing pigeon flights we show
that the more complex three dimensional models are capable of predicting and
simulating trajectories, as well as exhibiting realistic collective dynamics.
The simulations of the reconstructed models are used to extract properties of
the collective behavior in pigeons, and how it is affected by changing the
initial conditions of the system. Our results demonstrate that this approach
may be applied to construct models capable of simulating trajectories and
collective dynamics using experimental field measurements of herd movement.
From these models, the behavior of the individual agents (animals) may be
inferred
SLX4 Assembles a Telomere Maintenance Toolkit by Bridging Multiple Endonucleases with Telomeres
SummarySLX4 interacts with several endonucleases to resolve structural barriers in DNA metabolism. SLX4 also interacts with telomeric protein TRF2 in human cells. The molecular mechanism of these interactions at telomeres remains unknown. Here, we report the crystal structure of the TRF2-binding motif of SLX4 (SLX4TBM) in complex with the TRFH domain of TRF2 (TRF2TRFH) and map the interactions of SLX4 with endonucleases SLX1, XPF, and MUS81. TRF2 recognizes a unique HxLxP motif on SLX4 via the peptide-binding site in its TRFH domain. Telomeric localization of SLX4 and associated nucleases depend on the SLX4-endonuclease and SLX4-TRF2 interactions and the protein levels of SLX4 and TRF2. SLX4 assembles an endonuclease toolkit that negatively regulates telomere length via SLX1-catalyzed nucleolytic resolution of telomere DNA structures. We propose that the SLX4-TRF2 complex serves as a double-layer scaffold bridging multiple endonucleases with telomeres for recombination-based telomere maintenance
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