106 research outputs found
The resilience of interdependent transportation networks under targeted attack
Modern world builds on the resilience of interdependent infrastructures
characterized as complex networks. Recently, a framework for analysis of
interdependent networks has been developed to explain the mechanism of
resilience in interdependent networks. Here we extend this interdependent
network model by considering flows in the networks and study the system's
resilience under different attack strategies. In our model, nodes may fail due
to either overload or loss of interdependency. Under the interaction between
these two failure mechanisms, it is shown that interdependent scale-free
networks show extreme vulnerability. The resilience of interdependent SF
networks is found in our simulation much smaller than single SF network or
interdependent SF networks without flows.Comment: 5 pages, 4 figure
Analysis of Eco-Hydrological Characteristics of the Four Famous Carps' Spawning Grounds in the Middle Reach of Yangtze River
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data
As a harzard disaster, landslide often brings tremendous losses to humanity,
so it's necessary to achieve reliable detection of landslide. However, the
problems of visual blur and small-sized dataset cause great challenges for old
landslide detection task when using remote sensing data. To reliably extract
semantic features, a hyper-pixel-wise contrastive learning augmented
segmentation network (HPCL-Net) is proposed, which augments the local salient
feature extraction from the boundaries of landslides through HPCL and fuses the
heterogeneous infromation in the semantic space from High-Resolution Remote
Sensing Images and Digital Elevation Model Data data. For full utilization of
the precious samples, a global hyper-pixel-wise sample pair queues-based
contrastive learning method, which includes the construction of global queues
that store hyper-pixel-wise samples and the updating scheme of a momentum
encoder, is developed, reliably enhancing the extraction ability of semantic
features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide
dataset and experiment results show that the model greatly improves the
reliablity of old landslide detection compared to the previous old landslide
segmentation model, where mIoU metric is increased from 0.620 to 0.651,
Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is
increased from 0.501 to 0.565
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images
Huge challenges exist for old landslide detection because their morphology
features have been partially or strongly transformed over a long time and have
little difference from their surrounding. Besides, small-sample problem also
restrict in-depth learning.
In this paper, an iterative classification and semantic segmentation network
(ICSSN) is developed, which can greatly enhance both object-level and
pixel-level classification performance by iteratively upgrading the feature
extractor shared by two network. An object-level contrastive learning (OCL)
strategy is employed in the object classification sub-network featuring a
siamese network to realize the global features extraction, and a
sub-object-level contrastive learning (SOCL) paradigm is designed in the
semantic segmentation sub-network to efficiently extract salient features from
boundaries of landslides. Moreover, an iterative training strategy is
elaborated to fuse features in semantic space such that both object-level and
pixel-level classification performance are improved.
The proposed ICSSN is evaluated on the real landslide data set, and the
experimental results show that ICSSN can greatly improve the classification and
segmentation accuracy of old landslide detection. For the semantic segmentation
task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448,
the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381
to 0.3743, and the object-level detection accuracy of old landslides is
enhanced from 0.55 to 0.9. For the object classification task, the F1 score
increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to
0.8875
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