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1. Data-driven Bus Crowding Prediction Models Using Context-specific Features

   https://doi.org/10.1145/3406962  

   Arabghalizi, Tahereh ; Labrinidis, Alexandros ( October 2020 , ACM/IMS Transactions on Data Science)

   null (Ed.)

   Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times). 

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2. Context matters: changes in memory over a period of sleep are driven by encoding context

   https://doi.org/10.1101/lm.053634.122  

   Schechtman, Eitan ; Heilberg, Julia ; Paller, Ken A. ( January 2023 , Learning & Memory)

   During sleep, recently acquired episodic memories (i.e., autobiographical memories for specific events) are strengthened and transformed, a process termed consolidation. These memories are contextual in nature, with details of specific features interwoven with more general properties such as the time and place of the event. In this study, we hypothesized that the context in which a memory is embedded would guide the process of consolidation during sleep. To test this idea, we used a spatial memory task and considered changes in memory over a 10-h period including either sleep or wake. In both conditions, participants ( N = 62) formed stories that contextually bound four objects together and then encoded the on-screen spatial position of all objects. Results showed that the changes in memory over the sleep period were correlated among contextually linked objects, whereas no such effect was identified for the wake group. These results demonstrate that context-binding plays an important role in memory consolidation during sleep. 

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3. Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator

   https://doi.org/10.1109/LRA.2023.3273515  

   Ng, Jerry ; Asada, H. Harry ( July 2023 , IEEE Robotics and Automation Letters)
4. Pufferfish: Container-driven Elastic Memory Management for Data-intensive Applications

   https://doi.org/doi.org/10.1145/3357223.3362730  

   Chen, Wei ; Pi, Aidi ; Wang, Shaoqi ; Zhou, Xiaobo ( November 2019 , SoCC '19: ACM Proceedings of the ACM Symposium on Cloud Computing)

   Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution. 

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5. Pufferfish: Container-driven Elastic Memory Management for Data-intensive Applications

   https://doi.org/https://doi.org/10.1145/3357223.3362730  

   Chen, Wei ; Pi, Aidi ; Wang, Shaoqi ; Zhou, Xiaobo ( November 2019 , ACM SoCC '19: Proceedings of the ACM Symposium on Cloud Computing)

   Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution. 

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