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Mangroves have evolved at least 27 times across ~20 plant families to survive coastal. To environments characterized by high salinity, inundation, intense light, and strong winds survive these extreme conditions, mangroves exhibit a variety of physiological strategies to tolerate the low osmotic potentials associated with saltwater inundation. Because low osmotic potentials are counterbalanced by high turgor pressure, saltwater exposure exerts mechanical demands on cells. Analyzing 34 mangrove species and 33 closely related inland taxa from 17 plant families, we show that compared to their inland relatives, mangroves have unusually small leaf epidermal pavement cells and thicker cell walls, which together confer greater mechanical strength and tolerance to low osmotic potentials. However, mangroves do not exhibit smaller, more numerous stomata that enable higher photosynthetic rates , suggesting selection on biomechanical integrity rather than on gas exchange capacity. Notably, mangroves break the allometric scaling between the sizes of epidermal pavement cells and stomata typically seen in land plants, highlighting that strong selection in saline habitats can override genome size–mediated scaling rules. Phylogenetic comparative analyses revealed repeated convergent evolution of cell traits across independent transitions from inland to coastal habitats. These anatomical changes constitute a simple but effective adaptation to salt stress. Our findings underscore the role of biomechanics in driving convergent evolution of cell traits and suggest that manipulating cell size and wall properties could be a promising strategy to engineering salt-tolerant plants. 

Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector⁺, which can automatically and accurately detect and profile distributed solar PV arrays without any extra cost. In essence, SolarDetector⁺first leverages multiple data augmentation techniques, including Cycle-Consistent Adversarial Networks, Latent Diffusion Models, and Generative Adversarial networks, to build a large rooftop satellite imagery dataset (RSID). Then, SolarDetector⁺employs Mask R-convolutional neural networks algorithm to accurately identify rooftop solar PV arrays and learn the detailed installation information for each solar PV array simultaneously. We find that pre-trained SolarDetector⁺yields an average Matthews correlation coefficient of 0.862 to detect solar PV arrays over RSID, which is ∼50% better than the most recent open source detection system—SolarFinder. 

Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of 0.68 in a closed-world setting and infer voice command categories with an MCC of 0.84 in an open-world setting by eavesdropping VPN-encrypted network traffic data. This presents a significant risk to user privacy and security, as it suggests that external on-path attackers could still potentially intercept and decipher users’ voice commands despite the VPN encryption. We then further examine the sensitivity of voice speaker commands to VoiceAttack. We find that 134 voice speaker commands are highly vulnerable to VoiceAttack. We also present a defense approach—VoiceDefense, which could inject inject appropriate traffic “noise” into voice speaker traffic. And our evaluation results show that VoiceDefense could effectively mitigate VoiceAttack on Amazon Echo and Google Home.