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1. Event Trend Aggregation Under Rich Event Matching Semantics

   https://doi.org/10.1145/3299869.3319862  

   Poppe, Olga; Lei, Chuan; Rundensteiner, Elke A.; Maier, David (June 2019, SIGMOD)

   null (Ed.)

   Streaming applications from cluster monitoring to algorithmic trading deploy Kleene queries to detect and aggregate event trends. Rich event matching semantics determine how to compose events into trends. The expressive power of stateof- the-art streaming systems remains limited since they do not support many of these semantics. Worse yet, they suffer from long delays and high memory costs because they maintain aggregates at a fine granularity. To overcome these limitations, our Coarse-Grained Event Trend Aggregation (Cogra) approach supports a rich variety of event matching semantics within one system. Better yet, Cogra incrementally maintains aggregates at the coarsest granularity possible for each of these semantics. In this way, Cogra minimizes the number of aggregates – reducing both time and space complexity. Our experiments demonstrate that Cogra achieves up to six orders of magnitude speed-up and up to seven orders of magnitude memory reduction compared to state-of-the-art approaches. 

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2. Pre-Event Trends in the Panel Event-Study Design

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3. Improving Event Definition Following For Zero-Shot Event Detection

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5. Event Neural Networks

   Dutson, Matthew; Li, Yin; Gupta, Mohit (August 2022, Springer (European Conference on Computer Vision - ECCV))

   Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve considerable computation savings during video inference. A defining characteristic of EvNets is that each neuron has state variables that provide it with long-term memory, which allows low-cost, high-accuracy inference even in the presence of significant camera motion. We show that it is possible to transform a wide range of neural networks into EvNets without re-training. We demonstrate our method on state-of-the-art architectures for both high- and low-level visual processing, including pose recognition, object detection, optical flow, and image enhancement. We observe roughly an order-ofmagnitude reduction in computational costs compared to conventional networks, with minimal reductions in model accuracy. 

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