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Biometric authentication systems are increasingly needed across a broad range of applications including in smart city environments (e.g., entering hotels), and in smart home environments (e.g., controlling smart devices). Traditional methods, such as face-based and fingerprint-based authentication, usually incur high costs to be installed in all this kind of environments. In this paper, we develop a ubiquitous low-effort user authentication approach, mmPalm, based on palm recognition using millimeter wave (mmWave) signals. mmWave technology has been adopted by WiGig and 5G, making mmPalm a low-cost solution that can be widely adopted in public places. In addition, the high resolution of mmWave signals allows mmPalm to extract detailed palm characteristics (e.g., palm geometry, skin thickness, and texture) that can assemble distinctive palmprints for user authentication. Our innovative virtual antennas design further increases the spatial resolution of a commercial mmWave device, enabling it to fully capture the comprehensive palmprint features. Moreover, to address the challenge of small-scale environmental changes (e.g., variations in palm-device distances and palm orientations), we design a novel palm profile augmentation method, utilizing a Conditional Generative Adversarial Network (cGAN) to generate synthetic palm profiles for mitigating palm instability. Furthermore, we design a cross-environment adaptation framework based on transfer learning to address the challenge of large-scale environmental changes, including multipath variations introduced by human bodies and nearby furniture. Extensive experiments with 30 participants through 6 months demonstrate that mmPalm achieves 99% authentication accuracy with resilience against different types of attacks, including random, impersonation, and counterfeit. 

Coral reefs are facing threats from a variety of global change stressors, including ocean warming, acidification, and deoxygenation. It has been hypothesized that growing corals near primary producers such as macroalgae or seagrass may help to ameliorate acidification and deoxygenation stress, however few studies have explored this effect in situ. Here, we investigated differences in coral growth rates across a natural gradient in seawater temperature, pH, and dissolved oxygen (DO) variability in a nearshore seagrass bed on Dongsha Atoll, Taiwan, South China Sea. We observed strong spatial gradients in temperature (5°C), pH (0.29 pH units), and DO (129 μmol O₂kg^-1) across the 1-kilometer wide seagrass bed. Similarly, diel variability recorded by an autonomous sensor in the shallow seagrass measured diel ranges in temperature, pH, and DO of up to 2.6°C, 0.55, and 204 μmol O₂kg^-1, respectively. Skeletal cores collected from 15 massivePoritescorals growing in the seagrass bed at 4 sites revealed no significant differences in coral calcification rates between sites along the gradients. However, significant differences in skeletal extension rate and density suggest that the dynamic temperature, pH, and/or DO variability may have influenced these properties. The lack of differences in coral growth between sites may be because favorable calcification conditions during the day (high temperature, pH, and DO) were proportionally balanced by unfavorable conditions during the night (low temperature, pH, and DO). Alternatively, other factors were simply more important in controlling coral calcification and/or corals were acclimated to the prevailing conditions at each site. 

The lack of a detailed mechanistic understanding for plasmon-mediated charge transfer at metal-semiconductor interfaces severely limits the design of efficient photovoltaic and photocatalytic devices. A major remaining question is the relative contribution from indirect transfer of hot electrons generated by plasmon decay in the metal to the semiconductor compared to direct metal-to-semiconductor interfacial charge transfer. Here, we demonstrate an overall electron transfer efficiency of 44 ± 3% from gold nanorods to titanium oxide shells when excited on resonance. We prove that half of it originates from direct interfacial charge transfer mediated specifically by exciting the plasmon. We are able to distinguish between direct and indirect pathways through multimodal frequency-resolved approach measuring the homogeneous plasmon linewidth by single-particle scattering spectroscopy and time-resolved transient absorption spectroscopy with variable pump wavelengths. Our results signify that the direct plasmon-induced charge transfer pathway is a promising way to improve hot carrier extraction efficiency by circumventing metal intrinsic decay that results mainly in nonspecific heating. 

High-throughput screening and material informatics have shown a great power in the discovery of novel materials, including batteries, high entropy alloys, and photocatalysts. However, the lattice thermal conductivity ( κ ) oriented high-throughput screening of advanced thermal materials is still limited to the intensive use of first principles calculations, which is inapplicable to fast, robust, and large-scale material screening due to the unbearable computational cost demanding. In this study, 15 machine learning algorithms are utilized for fast and accurate κ prediction from basic physical and chemical properties of materials. The well-trained models successfully capture the inherent correlation between these fundamental material properties and κ for different types of materials. Moreover, deep learning combined with a semi-supervised technique shows the capability of accurately predicting diverse κ values spanning 4 orders of magnitude, especially the power of extrapolative prediction on 3716 new materials. The developed models provide a powerful tool for large-scale advanced thermal functional materials screening with targeted thermal transport properties.