Accurate and energy-efficient fall detection on commodity smartphones is crucial for long-term monitoring of older adults and high-risk populations. However, most existing methods emphasize window-level accuracy and F1 score, while largely overlooking probabilistic calibration and event-level behavior that are critical for real-world deployment. We propose LFNET, a lightweight multi-channel convolutional network for fall detection using a single waist-mounted smartphone accelerometer. LFNET integrates multiscale depthwise separable convolutions with efficient channel attention, and jointly leverages tri-axial accelerations and their magnitude to capture both posture dynamics and impact intensity. On the UniMiB-SHAR dataset, LFNET attains competitive or superior F1 and AUPRC on both the binary fall detection task (AF2) and the 17-class activity recognition task (AF17), while using only a fraction of the parameters and FLOPs of larger CNN and Transformer baselines. To better match deployment requirements, we apply temperature scaling and sensitivity-constrained threshold selection to calibrate predicted probabilities and choose operating points tailored to fall-risk preferences. Using semi-synthetic continuous streams constructed from UniMiB-SHAR segments, we further design an event-level post-processing pipeline combining K-consecutive positives, hysteresis thresholds, gap merging, and refractory periods. Event-level experiments show that, under a fixed configuration, LFNET improves event sensitivity from 90.1% to 96.4% over a CNN baseline, while reducing false alarms from 1.4 to 0.2 FA/h and shortening median detection delay from 1.4 s to 0.8 s. These results demonstrate that the proposed framework not only performs well on standard window-level metrics, but also offers favorable false-alarm–delay trade-offs for smartphone-based, long-term fall detection in simulated monitoring scenarios.

In large-scale enterprises, on-call engineers (OCEs) are critical for ensuring service availability and reliability. However, as incidents grow in volume and complexity, traditional manual on-call processes are becoming increasingly inadequate. Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and multi-agent collaboration, presenting new opportunities for automation. We propose OncallX, an end-to-end automated on-call system designed for real-world industrial scenarios that integrates LLMs with multi-agent cooperation to enable intelligent and efficient incident management. OncallX first enhances user queries by leveraging external knowledge bases and multi-turn dialogue interactions. Subsequently, multiple expert agents collaborate through tree-search-based mechanisms to generate effective responses and solutions. When incidents cannot be resolved automatically, OncallX accurately assigns them to the most appropriate teams. Comprehensive experiments conducted in the real-world production environment of a top-tier global online video service provider demonstrate that OncallX efficiently responds to incidents and accurately triages tickets, significantly outperforming existing methods in both automated metrics and human evaluations. Furthermore, OncallX has been successfully deployed in production for two months, during which it has substantially enhanced on-call efficiency, reducing average incident response time to just 21 seconds and average triage time to 4 seconds—representing a transformative improvement in operational excellence.

We present ATS/PML, a modeling language with an expressive type system (supporting both dependent types and linear types), and argue that the types in ATS/PML can be of great help in detecting modeling errors at compile-time. On one hand, we introduce modeling primitives with well-designed types into ATS/PML to facilitate a synergic combination of type-checking with model-checking. On the other hand, we compile ATS/PML into Promela so that the SPIN modelchecker can be readily employed to perform checking on models constructed in ATS/PML.