Utility-Aware Progressive Inference over UDP Packet Blocks for Emergency Communications

Emergency communications increasingly rely on remote visual inference for timely hazard detection under stringent bandwidth and latency constraints. However, conventional UDP-based visual delivery typically performs inference only after the full payload has been received, even though partially received packet blocks may already contain sufficient task-relevant evidence for reliable decision making. This paper proposes a utility-aware progressive inference framework for emergency communications, which operates directly on UDP packet blocks and determines when sufficient task value has been accumulated for early hazard recognition. Specifically, the sender estimates packet-level decision utility as lightweight control metadata, while the receiver progressively updates partial observations, accumulates the utility of received packets, and triggers an early stop once the normalized utility exceeds a prescribed threshold. Experiments on a fire-scene detection dataset show that, at the main operating point, the proposed method reduces the average packet budget by 34.2% and the decision delay by 1209.17 ms while retaining 91.5% of the full-reception match rate. The method also maintains its advantage over the stability-based baseline under moderate packet loss and different packet-arrival orders. These results demonstrate that packet-level utility provides an effective basis for communication-efficient and delay-aware hazard recognition over UDP-based emergency links.

Suggesting Code Edits in Interactive Machine Learning Notebooks Using Large Language Models

Machine learning developers frequently use interactive computational notebooks, such as Jupyter notebooks, to host code for data processing and model training. Jupyter notebooks provide a convenient tool for writing machine learning pipelines and interactively observing outputs, however, maintaining Jupyter notebooks, e.g., to add new features or fix bugs, can be challenging due to the length and complexity of the notebooks. Moreover, there is no existing benchmark related to developer edits on Jupyter notebooks. To address this, we present the first dataset of 48,398 Jupyter notebook edits derived from 20,095 revisions of 792 machine learning repositories on GitHub, and perform the first study of the using LLMs to predict code edits in Jupyter notebooks. Our dataset captures granular details of cell-level and line-level modifications, offering a foundation for understanding real-world maintenance patterns in machine learning workflows. We observed that the edits on Jupyter notebooks are highly localized, with changes averaging only 166 lines of code in repositories. While larger models outperform smaller counterparts in code editing, all models have low accuracy on our dataset even after finetuning, demonstrating the complexity of real-world machine learning maintenance tasks. Our findings emphasize the critical role of contextual information in improving model performance and point toward promising avenues for advancing large language models' capabilities in engineering machine learning code.