Timely nowcasting is critical for public safety during fast-evolving storms, where even short delays can trigger cascading failures—as in the October 2024 Spain flash flood that claimed over 90 lives within minutes. While radar offers reliable real-time sensing of atmospheric structure, models that collapse 3D volumes into 2D slices inevitably discard vertical information essential for capturing storm growth, phase transitions, and collapse. We introduce StormMind, a physically grounded framework that forecasts convective evolution by modeling causal interactions across stratified atmospheric layers. StormMind addresses two fundamental challenges:(1) the nonlinear, asynchronous coupling between low-, mid-, and high-level processes; and (2) reflectivity uncertainty, where storms with distinct vertical structures may appear deceptively similar on radar, masking their true phase and intensity. To tackle these issues, StormMind designs: i) a Convection Dynamics Extractor that models storm evolution from two complementary perspectives—horizontal morphology, capturing the spatial organization of physical processes within individual atmospheric layers, and vertical coupling, modeling energy exchanges across layers; and ii) a Convection Manifestation Reconstructor that adaptively fuses intra- and inter-layer signals, conditioned on the evolving storm state, to infer phase transitions (e.g., initiation, intensification, dissipation). Evaluated on the large-scale 3D-NEXRAD dataset (2020–2022, U.S.), StormMind outperforms strong baselines, achieving a 14.71% gain in CSI40. In real-world deployment with the Guangzhou Meteorological Bureau (Mar–May 2025), it improves CSI40 by 9.39% and boosts early-warning accuracy (98.33%)

In always-on HAR deployments, model accuracy erodes silently as domain shift accumulates over time. Addressing this challenge requires moving beyond one-off updates toward instance-driven adaptation from streaming data. However, continuous adaptation exposes a fundamental tension: systems must selectively learn from informative instances while actively forgetting obsolete ones under long-term, non-stationary drift. To address them, we propose CODA, a continuous online adaptation framework for mobile sensing. CODA introduces two synergistic components: (i) Cache-based Selective Assimilation, which prioritizes informative instances likely to enhance system performance under sparse supervision, and (ii) an Adaptive Temporal Retention Strategy, which enables the system to gradually forget obsolete instances as sensing conditions evolve. By treating adaptation as a principled cache evolution rather than parameter-heavy retraining, CODA maintains high accuracy without model reconfiguration. We conduct extensive evaluations on four heterogeneous datasets spanning phone, watch, and multi-sensor configurations. Results demonstrate that CODA consistently outperforms one-off adaptation under non-stationary drift, remains robust against imperfect feedback, and incurs negligible on-device latency.

Continuous ECG monitoring is vital for early detection of arrhythmias and other cardiac abnormalities—especially during sleep, when symptoms often go unnoticed—yet existing solutions remain expensive, obtrusive, and impractical for long-term daily use. Ballistocardiography (BCG)—a passive, contactless modality that captures cardiac-induced body motion—offers a compelling alternative. However, prior efforts treat ECG reconstruction as waveform regression, leading to overfitting to individual-specific or posture-dependent artifacts. Inspired by the fact that ECG and BCG reflect parallel structures in cardiac event sequences (e.g., P/QRS/T-waves vs. I/J-waves), we design Med2ECG: a structure-aligned system that reconstructs ECG from high-fidelity BCG by explicitly aligning their latent physiological events. Med2ECG incorporates three key designs: (i) Multi-Scale Feature Extractor captures hierarchical temporal dynamics, preserving clinically relevant fine-grained features; (ii) Shared-Personalized Experts employs a Mixture-of-Experts (MoE) to adaptively disentangle signal variations due to individual and environmental factors; (iii) Medical-Informed Strategies introduces a diagnostic-driven multi-objective loss, integrating structural alignment, morphological fidelity, and landmark-aware supervision to preserve clinically critical intervals. Experiments across public and self-collected in-hospital datasets (20 healthy individuals and 10 patients with diverse cardiovascular conditions), Med2ECG achieves > 0.92 Pearson correlation, < 15% amplitude error, and precise PR/QRS/QT/RR interval estimation within 5–20 ms—demonstrating strong generalization across subjects, postures, and environments.

Hypertension affects 1.28 billion adults, but blood pressure alone is insufficient for accurate hypertension detection. In this paper, we identified a range of multi-dimensional hemodynamics as novel biomarkers that can offer more comprehensive cardiovascular insights for detecting hypertension. We design a novel system that collects ballistocardiogram (BCG) signals from an optic fiber sensor mat. It features a multi-task, multi-branch, unsupervised domain adaptation learning framework, enabling simultaneous prediction of five hemodynamic biomarkers for hypertension detection. In a 4-month trial with 85 subjects, Hyde achieved 97.65% average accuracy for hypertension detection.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand "landmarks'', especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, "overclocks'' its sampling rate to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96%) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.4%), and under various practical usage disturbances.

Localization is a fundamental problem in wireless sensor networks and its accuracy impacts the efficiency of location-aware protocols and applications, such as routing and storage. Most previous localization algorithms assume that sensors are distributed in regular areas without holes or obstacles, which often does not reflect real-world conditions, especially for outdoor deployment of wireless sensor networks. In this paper, we propose a novel scheme called reliable anchor-based localization (RAL), which can greatly reduce the localization error due to the irregular deployment areas. We first provide theoretical analysis of the minimum hop length for uniformly distributed networks and then show its close approximation to empirical results, which can assist in the construction of a reliable minimal hop-length table offline. Using this table, we are able to tell whether a path is severely detoured and compute a more accurate average hop length as the basis for distance estimation. At runtime, the RAL scheme 1) utilizes the reliable minimal hop length from the table as the threshold to differentiate between reliable anchors and unreliable ones, and 2) allows each sensor to determine its position utilizing only distance constraints obtained from reliable anchors. The simulation results show that RAL can effectively filter out unreliable anchors and therefore improve the localization accuracy.

ShanghaiGrid is the first metropolitan grid in China. The project aims to develop an environment to share distributed resources in Shanghai conveniently based on grid technology. Yet, resources are finite for ever, especially for some expensive and specific resources. Users must reserve these resources in advance before use them. Present solutions need users confirm the absolute start time of reservation before the reservation which is impossible in practice at most time. But they can often estimate the relative time to start reservation after a specific event, e.g. beginning or finishing a task. As a part of ShanghaiGrid, we develop an ECA rule-based workflow management system (EWMS) which can serve for arranging the relative start time of advance resource reservation as users' demand. We introduce the design architecture of EWMS in the paper, and give a modeling demo to show how to realize advance resource reservation through our system