Microbes have engaged in antagonistic associations with plants for hundreds of millions of years. Plants, in turn, have evolved diverse immune strategies to combat microbial pathogens. The conflicts ...between plants and pathogens result in everchanging coevolutionary cycles known as ‘Red Queen’ dynamics. These ancient and ongoing plant–pathogen interactions have shaped the evolution of both plant and pathogen genomes. With the recent explosion of plant genomescale data, comparative analyses provide novel insights into the coevolutionary dynamics of plants and pathogens. Here, we discuss the ancient associations between plants and microbes as well as the evolutionary principles underlying plant–pathogen interactions. We synthesize and review the current knowledge on the origin and evolution of key components of the plant immune system. We also highlight the importance of studying algae and nonflowering land plants in understanding the evolution of the plant immune system.
Existing inefficient traffic light cycle control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and ...dynamically adjusting the traffic light duration accordingly is a must. Existing works either split the traffic signal into equal duration or only leverage limited traffic information. In this paper, we study how to decide the traffic signal duration based on the collected data from different sensors. We propose a deep reinforcement learning model to control the traffic light cycle. In the model, we quantify the complex traffic scenario as states by collecting traffic data and dividing the whole intersection into small grids. The duration changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards. The proposed model incorporates multiple optimization elements to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation on a Simulation of Urban MObility simulator. Simulation results show the efficiency of our model in controlling traffic lights.
Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices ...are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their ...excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called SpectralFormer . Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse "soft" residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility.
In this paper, we study the architectures of space-air-ground integration network (SAGIN) proposed by domestic scientific research institutes, and put forward an collaborative federal learning ...architecture suitable for SAGIN to solve the problems of insecurity and low timeliness caused by traffic backhaul. An anomaly traffic detection method is proposed based on the requirements and characteristics of SAGIN. The problem that it is difficult to manually label and extract features in the traffic of SAGIN is solved through the improvement of deep learning algorithm. The challenge of lack of professionals labeling training set is solved by studying the method of semi supervision. The problem of artificial feature engineering is solved by studying the end-to-end anomaly traffic detection algorithm. Finally, we design a simulation environment for the anomaly traffic detection in SAGIN, and verify the feasibility and advanced nature of the proposed methods.
The pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has posed a severe threat to global public health. Yet, the origin of SARS-CoV-2 remains mysterious. Several recent studies ...(e.g., Lam et al.,Xiao et al.) identified SARS-CoV-2-related viruses in pangolins, providing novel insights into the evolution and diversity of SARS-CoV-2-related viruses.
The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end ...users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.
Due to the malicious attacks in wireless networks, physical layer security has attracted increasing concerns from both academia and industry. The research on physical layer security mainly focuses ...either on the secrecy capacity/achievable secrecy rate/capacity-equivocation region from the perspective of information theory, or on the security designs from the viewpoints of optimization and signal processing. Because of its importance in security designs, the latter research direction is surveyed in a comprehensive way in this paper. The survey begins with typical wiretap channel models to cover common scenarios and systems. The topics on physical-layer security designs are then summarized from resource allocation, beamforming/precoding, and antenna/node selection and cooperation. Based on the aforementioned schemes, the performance metrics and fundamental optimization problems are discussed, which are generally adopted in security designs. Thereafter, the state of the art of optimization approaches on each research topic of physical layer security is reviewed from four categories of optimization problems, such as secrecy rate maximization, secrecy outrage probability minimization, power consumption minimization, and secure energy efficiency maximization. Furthermore, the impacts of channel state information on optimization and design are discussed. Finally, the survey concludes with the observations on potential future directions and open challenges.
The ever-increasing demand for ubiquitous and differentiated services at anytime and anywhere emphasizes the necessity of aerospace integrated networks (AINs) which consist of multi-tiers, e.g., ...high-altitude platforms (HAPs) tier, unmanned aerial vehicles (UAVs) tier, low earth orbit (LEO) satellites tier, etc. for wide coverage and high capacity. However, the inherent heterogeneity, time-variability, and differentiated mobility characteristics between terrestrial networks and AINs constraint the materialization of the aforementioned widely expected potentials of AINs. Therefore, it is crucial to make a profound understanding of AINs' impacts on pivotal models and methodologies for enabling 6G, thus providing technology reference for AINs empowering 6G. To understand the latest development and ultimately open new research niches on this significant topic, this survey is the pioneer paper to serve as a systematical and comprehensive overview in both single-tier scenarios and combining-tiers scenarios in AINs. We start with a profound discussion about the state-of-the-art potentially promising methodologies and models in the era of AINs empowering 6G from the perspective of the system architecture and networking design and enabling technologies of AINs, to enable flexible and scalable management and control of the AINs and ensure network stability. Furthermore, we make an in-depth literature overview across the network dynamics modeling, theoretical performance analysis model, and system optimization to enhance the system performance and guarantee user-on-demand services. Additionally, we highlight the ongoing research challenges and future directions with focusing on the development trend of ultra-dense satellite constellations in 6G.
Jasmonates are phytohormones that modulate a wide spectrum of plant physiological processes, especially defense against herbivores and necrotrophs. The molecular mechanisms of jasmonate biosynthesis ...and signaling have been well characterized in model plants. In this review, we provide an in-depth analysis and overview of the origin and evolution of the jasmonate biosynthesis and signaling pathways. Furthermore, we discuss the striking parallels between jasmonate and auxin signaling mechanisms, which reveals a common ancestry of these signaling mechanisms. Finally, we highlight the importance of studying jasmonate biosynthesis and signaling in lower plants.
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