Profile-guided optimization (PGO) advances the frontiers of compiler optimization by leveraging dynamic runtime information to generate highly optimized binaries. Traditional instrumentation-based profiling collects accurate profile data but often suffers from heavy runtime overhead. In contrast, sampling-based profiling is more efficient and scalable when collecting profile data while avoiding intrusive source code modifications. However, accurately collecting execution profiles via sampling remains challenging, especially when applied to fully optimized binaries. Such inaccurate profile data can restrict the benefits of PGO. This paper presents ProfiX, a machine learning-guided approach based on hybrid GNN architecture that addresses the problem of profile inference, aiming to correct inaccuracies in the profiles collected by sampling. Experiments on the SPEC 2017 benchmarks demonstrate that ProfiX achieves up to a 9.15\% performance improvement compared to the state-of-the-art traditional algorithm and an average 6.26\% improvement over the baseline machine learning models. These results highlight the effectiveness of ProfiX in optimizing real-world application profiles.

Tracking the menstrual cycle (MC) is essential for women to manage their health and schedule, especially for those with irregular MC. Existing MC tracking methods either rely on length of previous cycles (e.g., calendar noting) or require additional devices to collect more information (e.g., basal temperature), which are not able to realize both accuracy and convenience. Inspired by the medical studies that gait patterns will be affected by MC, we design a smartphone-based application named MC-Tracking, which monitors MC based on the Inertial Measurement Unit (IMU) signals. By identifying the walking activity based on the acceleration and angular velocity signals, we train an attention-based prediction model that can be generalized to new users with meta learning. 40 volunteers participate in an extensive experiment for more than 3 months, in which more than 2.4 TB of time-series data is collected to evaluate the performance of MC-tracking. It is verified that MC-tracing can predict the onset of MC seven days in advance with an average error of 0.56 days. We also demonstrate that the prediction accuracy is robust to the age, emotion, biological clock and smartphone brand.