158 research outputs found
Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity
Future Internet-of-Things (IoT) will connect billions of small computing
devices embedded in the environment and support their device-to-device (D2D)
communication. Powering this massive number of embedded devices is a key
challenge of designing IoT since batteries increase the devices' form factors
and battery recharging/replacement is difficult. To tackle this challenge, we
propose a novel network architecture that enables D2D communication between
passive nodes by integrating wireless power transfer and backscatter
communication, which is called a wirelessly powered backscatter communication
(WP-BackCom) network. In the network, standalone power beacons (PBs) are
deployed for wirelessly powering nodes by beaming unmodulated carrier signals
to targeted nodes. Provisioned with a backscatter antenna, a node transmits
data to an intended receiver by modulating and reflecting a fraction of a
carrier signal. Such transmission by backscatter consumes orders-of-magnitude
less power than a traditional radio. Thereby, the dense deployment of
low-complexity PBs with high transmission power can power a large-scale IoT. In
this paper, a WP-BackCom network is modeled as a random Poisson cluster process
in the horizontal plane where PBs are Poisson distributed and active ad-hoc
pairs of backscatter communication nodes with fixed separation distances form
random clusters centered at PBs. The backscatter nodes can harvest energy from
and backscatter carrier signals transmitted by PBs. Furthermore, the
transmission power of each node depends on the distance from the associated PB.
Applying stochastic geometry, the network coverage probability and transmission
capacity are derived and optimized as functions of backscatter parameters,
including backscatter duty cycle and reflection coefficient, as well as the PB
density. The effects of the parameters on network performance are
characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless
Communicatio
Testing of high current transformer by non-uniform equivalent magnetomotive force method
Peer Reviewe
A Quadratic Synchronization Rule for Distributed Deep Learning
In distributed deep learning with data parallelism, synchronizing gradients
at each training step can cause a huge communication overhead, especially when
many nodes work together to train large models. Local gradient methods, such as
Local SGD, address this issue by allowing workers to compute locally for
steps without synchronizing with others, hence reducing communication
frequency. While has been viewed as a hyperparameter to trade optimization
efficiency for communication cost, recent research indicates that setting a
proper value can lead to generalization improvement. Yet, selecting a
proper is elusive. This work proposes a theory-grounded method for
determining , named the Quadratic Synchronization Rule (QSR), which
recommends dynamically setting in proportion to as the
learning rate decays over time. Extensive ImageNet experiments on ResNet
and ViT show that local gradient methods with QSR consistently improve the test
accuracy over other synchronization strategies. Compared with the standard data
parallel training, QSR enables Local AdamW on ViT-B to cut the training time on
16 or 64 GPUs down from 26.7 to 20.2 hours or from 8.6 to 5.5 hours and, at the
same time, achieves or higher top-1 validation accuracy
Fucoxanthin attenuates LPS-induced acute lung injury via inhibition of the TLR4/MYD88 signaling axis
Acute lung injury (ALI) is a critical clinical condition with a high mortality rate. It is believed that the inflammatory storm is a critical contributor to the occurrence of ALI. Fucoxanthin is a natural extract from marine seaweed with remarkable biological properties, including antioxidant, anti-tumor, and anti-obesity. However, the anti-inflammatory activity of Fucoxanthin has not been extensively studied. The current study aimed to elucidate the effects and the molecular mechanism of Fucoxanthin on lipopolysaccharide-induced acute lung injury. In this study, Fucoxanthin efficiently reduced the mRNA expression of pro-inflammatory factors, including IL-10, IL-6, iNOS, and Cox-2, and down-regulated the NF-kappaB signaling pathway in Raw264.7 macrophages. Furthermore, based on the network pharmacological analysis, our results showed that anti-inflammation signaling pathways were screened as fundamental action mechanisms of Fucoxanthin on ALI. Fucoxanthin also significantly ameliorated the inflammatory responses in LPS-induced ALI mice. Interestingly, our results revealed that Fucoxanthin prevented the expression of TLR4/MyD88 in Raw264.7 macrophages. We further validated Fucoxanthin binds to the TLR4 pocket using molecular docking simulations. Altogether, these results suggest that Fucoxanthin suppresses the TLR4/MyD88 signaling axis by targeting TLR4, which inhibits LPS-induced ALI, and fucoxanthin inhibition may provide a novel strategy for controlling the initiation and progression of ALI
Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
Open-source software (OSS) supply chain enlarges the attack surface, which
makes package registries attractive targets for attacks. Recently, package
registries NPM and PyPI have been flooded with malicious packages. The
effectiveness of existing malicious NPM and PyPI package detection approaches
is hindered by two challenges. The first challenge is how to leverage the
knowledge of malicious packages from different ecosystems in a unified way such
that multi-lingual malicious package detection can be feasible. The second
challenge is how to model malicious behavior in a sequential way such that
maliciousness can be precisely captured. To address the two challenges, we
propose and implement Cerebro to detect malicious packages in NPM and PyPI. We
curate a feature set based on a high-level abstraction of malicious behavior to
enable multi-lingual knowledge fusing. We organize extracted features into a
behavior sequence to model sequential malicious behavior. We fine-tune the BERT
model to understand the semantics of malicious behavior. Extensive evaluation
has demonstrated the effectiveness of Cerebro over the state-of-the-art as well
as the practically acceptable efficiency. Cerebro has successfully detected 306
and 196 new malicious packages in PyPI and NPM, and received 385 thank letters
from the official PyPI and NPM teams
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