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With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace. However, inference that needs to largely take place on the `edge' (not processed on servers), is a highly computational and memory intensive workload, making it intractable for low-power mobile nodes and remote security applications. To address this challenge, this paper proposes a low-power (5W) end-to-end neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking is implemented using the apparent object velocity while tackling occlusion scenarios. For low-power classification of the tracked objects, the event camera is interfaced to IBM TrueNorth, which is time-multiplexed to tackle up to eight instances for a traffic monitoring application. The frame-based object track input is converted back to spikes for Truenorth classification via the energy efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TrueNorth model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the efficacy of our system to handle practical surveillance scenarios. Finally, we compare the proposed methodologies to state-of-the-art event-based systems for object tracking and classification, and demonstrate the use case of our neuromorphic approach for low-power applications without sacrificing on performance.

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Open world database management systems assume tuples not in the database still exist and are becoming an increasingly important area of research. We present Themis, the first open world database that automatically rebalances arbitrarily biased samples to approximately answer queries as if they were issued over the entire population. We leverage apriori population aggregate information to develop and combine two different approaches for automatic debiasing: sample reweighting and Bayesian network probabilistic modeling. We build a prototype of Themis and demonstrate that Themis achieves higher query accuracy than the default AQP approach, an alternative sample reweighting technique, and a variety of Bayesian network models while maintaining interactive query response times. We also show that \name is robust to differences in the support between the sample and population, a key use case when using social media samples.

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Open world database management systems assume tuples not in the database still exist and are becoming an increasingly important area of research. We present Themis, the first open world database that automatically rebalances arbitrarily biased samples to approximately answer queries as if they were issued over the entire population. We leverage apriori population aggregate information to develop and combine two different approaches for automatic debiasing: sample reweighting and Bayesian network probabilistic modeling. We build a prototype of Themis and demonstrate that Themis achieves higher query accuracy than the default AQP approach, an alternative sample reweighting technique, and a variety of Bayesian network models while maintaining interactive query response times. We also show that \name is robust to differences in the support between the sample and population, a key use case when using social media samples.

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