The University of Pennsylvania (Penn), founded in 1740 by Benjamin Franklin, is a private Ivy League research university in Philadelphia. It is renowned for its integrated resources across four undergraduate and 12 graduate schools, offering an unparalleled education informed by inclusivity, intellectual rigor, research, and a commitment to creating new knowledge for the benefit of society. NEC Labs America works with the University of Pennsylvania to advance AI-driven network optimization and energy-efficient communication protocols. Our collaboration strengthens infrastructure for edge computing and real-time decision-making in constrained environments. Please read about our latest news and collaborative publications with the University of Pennsylvania.

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Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport

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We aim to address key challenges in long-horizon embodied exploration and navigation by proposing a long-horizon object transport task called Long-HOT and a novel modular framework for temporally extended navigation. Agents in Long-HOT need to efficiently find and pick up target objects that are scattered in the environment, carry them to a goal location with load constraints, and optionally have access to a container. We propose a modular topological graph-based transport policy (HTP) that explores efficiently with the help of weighted frontiers. Our hierarchical approach uses a combination of motion planning algorithms to reach point goals within explored locations and object navigation policies for moving towards semantic targets at unknown locations. Experiments on both our proposed Habitat transport task and on MultiOn benchmarks show that our method outperforms baselines and prior works. Further, we analyze the agent’s behavior for the usage of the container and demonstrate meaningful generalization to harder transport scenes with training only on simpler versions of the task.

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Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

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Forecasting on Sparse Multivariate Time Series Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS’s individually, and do not leverage the dynamic distributions underlying the MTS’s, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting time series. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.

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