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Complex events (CEs) play a crucial role in CPS-IoT applications, enabling high-level decision-making in domains such as smart monitoring and autonomous systems. However, most existing models focus on short-span perception tasks, lacking the long-term reasoning required for CE detection. CEs consist of sequences of short-time atomic events (AEs) governed by spatiotemporal dependencies. Detecting them is difficult due to long, noisy sensor data and the challenge of filtering out irrelevant AEs while capturing meaningful patterns. This work explores CE detection as a case study for CPS-IoT foundation models capable of long-term reasoning. We evaluate three approaches: (1) leveraging large language models (LLMs), (2) employing various neural architectures that learn CE rules from data, and (3) adopting a neurosymbolic approach that integrates neural models with symbolic engines embedding human knowledge. Our results show that the state-space model, Mamba, which belongs to the second category, outperforms all methods in accuracy and generalization to longer, unseen sensor traces. These findings suggest that state-space models could be a strong backbone for CPS-IoT foundation models for long-span reasoning tasks. 

Recent advancements in large language models have spurred significant developments in Time Series Foundation Models (TSFMs). These models claim great promise in performing zero-shot forecasting without the need for specific training, leveraging the extensive "corpus" of time-series data they have been trained on. Forecasting is crucial in predictive building analytics, presenting substantial untapped potential for TSFMS in this domain. However, time-series data are often domain-specific and governed by diverse factors such as deployment environments, sensor characteristics, sampling rate, and data resolution, which complicates generalizability of these models across different contexts. Thus, while language models benefit from the relative uniformity of text data, TSFMs face challenges in learning from heterogeneous and contextually varied time-series data to ensure accurate and reliable performance in various applications. This paper seeks to understand how recently developed TSFMs perform in the building domain, particularly concerning their generalizability. We benchmark these models on three large datasets related to indoor air temperature and electricity usage. Our results indicate that TSFMs exhibit marginally better performance compared to statistical models on unseen sensing modality and/or patterns. Based on the benchmark results, we also provide insights for improving future TSFMs on building analytics. 

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduceTinyNS, the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators.TinyNSprovides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models.TinyNSuses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability,TinyNStalks to the target hardware during the optimization process. We showcase the utility ofTinyNSby deploying microcontroller-class neurosymbolic models through several case studies. In all use cases,TinyNSoutperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.