Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Fuzz testing embedded OSs is difficult because their implementations vary widely and rely on specialized hardware. These factors render many existing methods ineffective, since they prevent adapting established fuzzing routines, disrupt communication with the target OS, and impede observation of runtime behavior. This paper introduces EOF, a feedback-guided fuzzer designed to test embedded OSs running on actual hardware. Through the debug port, EOF communicates with the target embedded OS, executes test cases, and collect feedback data, with no dependence on OS services. Then, EOF deploys a cross-platform agent and executes API-aware input across diverse hardware. Last, EOF collects runtime coverage and critical execution events to identify interesting seeds and find potential bugs during fuzzing. We implemented EOF and evaluated its performance on four different embedded OSs, where EOF discovered 19 bugs and achieved a 50.84% coverage improvement on average compared with other comparable fuzzing methods.

Machine unlearning is becoming increasingly important as deep models become more prevalent, particularly when there are frequent requests to remove the influence of specific training data due to privacy concerns or erroneous sensing signals. Spatial-temporal Graph Neural Networks, in particular, have been widely adopted in real-world applications that demand efficient unlearning, yet research in this area remains in its early stages. In this paper, we introduce STEPS, a framework specifically designed to address the challenges of spatio-temporal graph unlearning. Our results demonstrate that STEPS not only ensures data continuity and integrity but also significantly reduces the time required for unlearning, while minimizing the accuracy loss in the new model compared to a model with 0% unlearning.

Deploying Large Language Models (LLMs) on resource-constrained devices remains challenging due to limited memory, lack of GPUs, and the complexity of existing runtimes. In this paper, we introduce TranSQL + , a template-based code generator that translates LLM computation graphs into pure SQL queries for execution in relational databases. Without relying on external libraries, TranSQL + , leverages mature database features-such as vectorized execution and out-of-core processing-for efficient inference. We further propose a row-to-column (ROW2COL) optimization that improves join efficiency in matrix operations. Evaluated on Llama3-8B and DeepSeekMoE models, TranSQL + achieves up to 20× lower prefill latency and 4× higher decoding speed compared to DeepSpeed Inference and Llama.cpp in low-memory and CPU-only configurations. Our results highlight relational databases as a practical environment for LLMs on low-resource hardware.

Wastewater infrastructures are vital in urban cities, but aging sanitary sewer systems face issues like cracked pipes and damaged manholes, leading to infiltration and inflow problems. Climate change contributes to more frequent heavy precipitation, potentially causing significant sewer overflows. These overflows can endanger public health by carrying disease-causing microorganisms, pathogens, chemicals, and pollutants. To improve the maintenance of wastewater systems, which often face challenges of sparse sensor coverage in the field, we develop a graph dataset and a spatio-temporal hydraulic model - HydroNet - tailored to urban wastewater systems. The trained HydroNet is able to predict temporal water depth, which can help identify potential infiltration, leading to an automated urban wastewater system with minimal time and labor costs.

This paper presents a fast charging system for smart devices. A novel scheme of dynamically adjusted charging current based on voltage-current dual-loop control is employed to fully utilize the power of the adaptor and maximize the charging speed. Simulation and experiment show that at least 12% more battery can be charged by the proposed method within 5 minutes of fast charging, compared to the conventional fast charging scheme.