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

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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 $H$ steps without synchronizing with others, hence reducing communication frequency. While $H$ has been viewed as a hyperparameter to trade optimization efficiency for communication cost, recent research indicates that setting a proper $H$ value can lead to generalization improvement. Yet, selecting a proper $H$ is elusive. This work proposes a theory-grounded method for determining $H$, named the Quadratic Synchronization Rule (QSR), which recommends dynamically setting $H$ in proportion to $\frac{1}{\eta^2}$ as the learning rate $\eta$ 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 $1.16\%$ or $0.84\%$ higher top-1 validation accuracy

Potassium fertilizer promotes the thin-shelled Tartary buckwheat yield by delaying senescence and promoting grain filling

The application rate of potassium fertilizer is closely related to the yield of crops. Thin-shelled Tartary buckwheat is a new variety of Tartary buckwheat with the advantages of thin shell and easy shelling. However, little is known about application rate of potassium fertilizer on the yield formation of thin-shelled Tartary buckwheat. This study aimed to clarify the effect of potassium fertilizer on the growth and yield of thin-shelled Tartary buckwheat. A field experiment to investigate the characteristics was conducted across two years using thin-shelled Tartary buckwheat (Miku 18) with four potassium fertilizer applications including 0 (no potassium fertilizer, CK), 15 (low-concentration potassium fertilizer, LK), 30 (medium-concentration potassium fertilizer, MK), and 45 kg·ha−1 (high-concentration potassium fertilizer, HK). The maximum and average grain filling rates; starch synthase activity; superoxide dismutase and peroxidase activities in leaves; root morphological indices and activities; available nitrogen, phosphorus, and organic matter content in rhizosphere soil; urease and alkaline phosphatase activities in rhizosphere soil; plant height, main stem node number, main stem branch number, leaf number; grain number per plant, grain weight per plant, and 100-grain weight increased first and then decreased with the increase in potassium fertilizer application rate and reached the maximum at MK treatment. The content of malondialdehyde was significantly lower in MK treatment than in other three treatments. The yields of thin-shelled Tartary buckwheat treated with LK, MK, and HK were 1.22, 1.37, and 1.07 times that of CK, respectively. In summary, an appropriate potassium fertilizer treatment (30kg·ha−1) can delay the senescence, promote the grain filling, and increase the grain weight and final yield of thin-shelled Tartary buckwheat. This treatment is recommended to be used in production to achieve high-yield cultivation of thin-shelled Tartary buckwheat

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