19 research outputs found
The Effect of Network Topology on Credit Network Throughput
Credit networks rely on decentralized, pairwise trust relationships
(channels) to exchange money or goods. Credit networks arise naturally in many
financial systems, including the recent construct of payment channel networks
in blockchain systems. An important performance metric for these networks is
their transaction throughput. However, predicting the throughput of a credit
network is nontrivial. Unlike traditional communication channels, credit
channels can become imbalanced; they are unable to support more transactions in
a given direction once the credit limit has been reached. This potential for
imbalance creates a complex dependency between a network's throughput and its
topology, path choices, and the credit balances (state) on every channel. Even
worse, certain combinations of these factors can lead the credit network to
deadlocked states where no transactions can make progress. In this paper, we
study the relationship between the throughput of a credit network and its
topology and credit state. We show that the presence of deadlocks completely
characterizes a network's throughput sensitivity to different credit states.
Although we show that identifying deadlocks in an arbitrary topology is
NP-hard, we propose a peeling algorithm inspired by decoding algorithms for
erasure codes that upper bounds the severity of the deadlock. We use the
peeling algorithm as a tool to compare the performance of different topologies
as well as to aid in the synthesis of topologies robust to deadlocks
Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture.
Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this article, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted Adaptive Instance Normalization (AdaIN) (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at https://github.com/huanghsheng/object-based-attention-mechanis
Strategic Latency Reduction in Blockchain Peer-to-Peer Networks
Most permissionless blockchain networks run on peer-to-peer (P2P) networks,
which offer flexibility and decentralization at the expense of performance
(e.g., network latency). Historically, this tradeoff has not been a bottleneck
for most blockchains. However, an emerging host of blockchain-based
applications (e.g., decentralized finance) are increasingly sensitive to
latency; users who can reduce their network latency relative to other users can
accrue (sometimes significant) financial gains. In this work, we initiate the
study of strategic latency reduction in blockchain P2P networks. We first
define two classes of latency that are of interest in blockchain applications.
We then show empirically that a strategic agent who controls only their local
peering decisions can manipulate both types of latency, achieving 60\% of the
global latency gains provided by the centralized, paid service bloXroute, or,
in targeted scenarios, comparable gains. Finally, we show that our results are
not due to the poor design of existing P2P networks. Under a simple network
model, we theoretically prove that an adversary can always manipulate the P2P
network's latency to their advantage, provided the network experiences
sufficient peer churn and transaction activity
Loss Analysis of Magnetic Gear with Slotted in Magnetic Modulation Ring
In order to improve the operation efficiency of coaxial magnetic gear (CMG), in this paper, a CMG model with slotted in magnetic modulation ring is proposed. In this model, the permanent magnets (PMs) of internal and external rotors are distributed in Halbach array, the inner rotor PMs are equally divided into 3 small pieces, and the outer rotor PMs are equally divided into 2 small pieces. At the same time, the static magnetic modulation ring iron blocks are slotted, each iron block has 3 slots, the width of the slot is 0.4°, and the depth of the single side slot is 1mm. Finally, a two-dimensional model is established, and the eddy current loss and iron loss of the model are optimized, compared with the conventional CMG model, it is found that the changed pattern can increase the internal and external output torque by 4% and 4.12%, respectively. The eddy current loss is reduced by 66.57%, and the iron loss is reduced by 8.9%, which significantly improve the operation efficiency of the CMG
Transcriptome sequencing provides evidence of genetic assimilation in a toad-headed lizard at high altitude
Understanding how organisms adapt to the environment is a compelling question in modern evolutionary biology. Genetic assimilation provides an alternative hypothesis to explain adaptation, in which phenotypic plasticity is first triggered by environmental factors, followed by selection on genotypes that reduce the plastic expression of phenotypes. To investigate the evidence of genetic assimilation in a high-altitude dweller, the toad-headed agama Phr ynocephalus vlangalii, we conducted a translocation experiment by moving individuals from high-to low-altitude environments. We then measured their gene expression profiles by transcriptome sequencing in heart, liver and muscle, and compared them to two low-altitude species P. axillaris and P. fors ythii. The results showed that the general expression profile of P. vlangalii was similar to its viviparous relative P. fors ythii, however, the differentially expressed genes in the liver of P. vlangalii showed a distinct pattern compared to both the low-altitude species. In particular, several key genes (FASN, ACAA2 and ECI2) within fatty acid metabolic pathway were no longer differentially expressed in P. valgnalii, suggesting the loss of plasticity for this pathway after translocation. This study provides evidence of genetic assimilation in fatty acid metabolism that may have facilitated the adaptation to high-altitude for P. vlangalii
Raft-Forensics: High Performance CFT Consensus with Accountability for Byzantine Faults
Crash fault tolerant (CFT) consensus algorithms are commonly used in
scenarios where system components are trusted, such as enterprise settings. CFT
algorithms offer high throughput and low latency, making them an attractive
option for centralized operations that require fault tolerance. However, CFT
consensus is vulnerable to Byzantine faults, which can be introduced by a
single corrupt component. Such faults can break consensus in the system.
Byzantine fault tolerant (BFT) consensus algorithms withstand Byzantine faults,
but they are not as competitive with CFT algorithms in terms of performance. In
this work, we explore a middle ground between BFT and CFT consensus by
exploring the role of accountability in CFT protocols. That is, if a CFT
protocol node breaks protocol and affects consensus safety, we aim to identify
which node was the culprit. Based on Raft, one of the most popular CFT
algorithms, we present Raft-Forensics, which provides accountability over
Byzantine faults. We theoretically prove that if two honest components fail to
reach consensus, the Raft-Forensics auditing algorithm finds the adversarial
component that caused the inconsistency. In an empirical evaluation, we
demonstrate that Raft-Forensics performs similarly to Raft and significantly
better than state-of-the-art BFT algorithms. With 256 byte messages,
Raft-Forensics achieves peak throughput 87.8% of vanilla Raft at 46% higher
latency, while state-of-the-art BFT protocol Dumbo-NG only achieves 18.9% peak
throughput at nearly higher latency
Multiscale voting mechanism for rice leaf disease recognition under natural field conditions.
Rice leaf disease (RLD) is one of the major factors that cause the decline in production, and the automatic recognition of such diseases under natural field conditions is of great significance for timely targeted rice management. Although many machine learning approaches have been proposed for RLD recognition, scale variation is still a challenging problem that affects prediction accuracy, especially in uncontrolled environments, such as natural fields. Also, the existing RLD data sets are collected in laboratory environments or with a constant scale, which cannot be used to develop the RLD classification algorithms under natural field conditions. To tackle these particular challenges, we propose a multiscale voting mechanism for RLD recognition under natural field conditions. First, data from 26 rice fields were collected to build a data set containing 6046 images of RLD. Afterwards, a feature pyramid was embedded into a mainstream classification architecture (EfficientNet) with a bottom-up and top-down pathway for feature fusion at different scales. To further reduce the inconsistency among multiscaled features, a multiscale voting strategy with regard to probability distribution was proposed to integrate the decisions from various scales. Each proposed module was carefully validated through an ablation study to demonstrate its effectiveness, and the proposed method was compared with a few state-of-the-art algorithms, including the Single Shot MultiBox Detector, Feature Pyramid Networks, Path Aggregation Network, and Bidirectional Feature Pyramid Network. Experimental results have shown that the classification accuracy of our model can reach 90.24%, which is 4.48% higher than that of the original EfficientNet-b0 model and 1.08% higher than that of existing multiscale networks. Finally, we exploit and demonstrate a visualized explanation for the boosted performance from the proposed model. As an extra outcome, our data set and codes are available at http://github.com/huanghsheng/multiscale-voting-mechanism to benefit the whole research community