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Vital signs monitoring has gained increasing attention due to its ability to indicate various human health and well-being conditions. The development of WiFi sensing technologies has made it possible to monitor vital signs using ubiquitous WiFi signals and devices. However, most existing approaches are dedicated to single-person scenarios. A few WiFi sensing approaches can achieve multi-person vital signs monitoring, whereas they are not identity-aware and sensitive to interferences in the environment. In this paper, we propose SpaceBeat, an identity-aware and interference-robust multi-person vital sign monitoring system using commodity WiFi. In particular, our system separates multiple people and locates each person in the spatial domain by leveraging multiple antennas. We analyze the change of signals at the location of each person to achieve identity-aware vital signs monitoring. We also design a contrastive principal component analysis-contrastive learning framework to mitigate interferences caused by other moving people. We evaluate SpaceBeat in various challenging environments, including interference scenarios, non-line-of-sight scenarios, different distances, etc. Our system achieves an average accuracy of 99.1% for breathing monitoring and 97.9% for heartbeat monitoring. 

Wind, wave, and acoustic observations are used to test a scaling for ambient sound levels in the ocean that is based on wind speed and the degree of surface wave development (at a given wind speed). The focus of this study is acoustic frequencies in the range 1-20 kHz, for which sound is generated by the bubbles injected during surface wave breaking. Traditionally, ambient sound spectra in this frequency range are scaled by wind speed alone. In this study, we investigate a secondary dependence on surface wave development. For any given wind-speed, ambient sound levels are separated into conditions in which waves are 1) actively developing or 2) fully developed. Wave development is quantified using the non-dimensional wave height, a metric commonly used to analyze fetch or duration limitations in wave growth. This simple metric is applicable in both coastal and open ocean environments. Use of the wave development metric to scale sound spectra is first motivated with observations from a brief case study near the island of Jan Mayen (Norwegian Sea), then robustly tested with long time-series observations of winds and waves at Ocean Station Papa (North Pacific Ocean). When waves are actively developing, ambient sound levels are elevated 2-3 dB across the 1-20 kHz frequency range. This result is discussed in the context of sound generation during wave breaking and sound attenuation by persistent bubble layers. 

Ear wearables (earables) are emerging platforms that are broadly adopted in various applications. There is an increasing demand for robust earables authentication because of the growing amount of sensitive information and the IoT devices that the earable could access. Traditional authentication methods become less feasible due to the limited input interface of earables. Nevertheless, the rich head-related sensing capabilities of earables can be exploited to capture human biometrics. In this paper, we propose EarSlide, an earable biometric authentication system utilizing the advanced sensing capacities of earables and the distinctive features of acoustic fingerprints when users slide their fingers on the face. It utilizes the inward-facing microphone of the earables and the face-ear channel of the ear canal to reliably capture the acoustic fingerprint. In particular, we study the theory of friction sound and categorize the characteristics of the acoustic fingerprints into three representative classes, pattern-class, ridge-groove-class, and coupling-class. Different from traditional fingerprint authentication only utilizes 2D patterns, we incorporate the 3D information in acoustic fingerprint and indirectly sense the fingerprint for authentication. We then design representative sliding gestures that carry rich information about the acoustic fingerprint while being easy to perform. It then extracts multi-class acoustic fingerprint features to reflect the inherent acoustic fingerprint characteristic for authentication. We also adopt an adaptable authentication model and a user behavior mitigation strategy to effectively authenticate legit users from adversaries. The key advantages of EarSlide are that it is resistant to spoofing attacks and its wide acceptability. Our evaluation of EarSlide in diverse real-world environments with intervals over one year shows that EarSlide achieves an average balanced accuracy rate of 98.37% with only one sliding gesture. 

This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system. 

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies onad hockernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns. 

Abstract Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co‐occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well‐characterized by the set of functional traits generally measured by ecologists.To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co‐occurrence in the hyper‐diverse genusFicus. Our study focused on over 800 genes related to drought and defence, providing detailed information on how these genes may contribute to the diversity ofFicusspecies.We find widespread and consistent evidence of the importance of defence gene dissimilarity in co‐occurring species, providing genetic support for what would be expected under the Janzen‐Connell mechanism. We also find that drought‐related gene sequence similarity is related toFicusco‐occurrence, indicating that similar responses to drought promote co‐occurrence.Synthesis. We provide the first detailed functional genomic evidence of how drought‐ and defence‐related genes simultaneously contribute to the local co‐occurrence in a hyper‐diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co‐occurrence in hyper‐diverse tropical tree genera.