Event temporal relation extraction (ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label.This formulation ignores the meaning of relations and wipes out their intrinsic dependency.After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events.For example, relation Includes could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs.Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3% on both datasets.By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios.When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations.The code is released at https://github.com/AndrewZhe/ A-Unified-Framework-for-ETRE.

High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. Our key technique, called adaptive zooming, dynamically adjusts the granularity of counters to maximize accuracy while minimizing memory usage. By applying MicroscopeSketch to three specific tasks---frequency estimation, top-k frequent items discovery, and top-k heavy changes identification-we demonstrate substantial improvements over existing methods, reducing errors by roughly 4 times for frequency estimation and 3 times for identifying top-k items. The relevant source code is available in a GitHub repository.