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1. Origin of exponential growth in nonlinear reaction networks

   https://doi.org/10.1073/pnas.2013061117  

   Lin, Wei-Hsiang; Kussell, Edo; Young, Lai-Sang; Jacobs-Wagner, Christine (November 2020, Proceedings of the National Academy of Sciences of the United States of America)

   null (Ed.)

   Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlin- ear systems is mathematically unclear. Here, we describe a general theoretical framework that reveals underlying principles of long- term growth: scalability of flux functions and ergodicity of the rescaled systems. Our theory shows that nonlinear fluxes can gen- erate not only balanced growth but also oscillatory or chaotic growth modalities, explaining nonequilibrium dynamics observed in cell cycles and ecosystems. Our mathematical framework is broadly useful in predicting long-term growth rates from natural and synthetic networks, analyzing the effects of system noise and perturbations, validating empirical and phenomenological laws on growth rate, and studying autocatalysis and network evolution. 

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2. Model-assisted deep learning of rare extreme events from partial observations

   https://doi.org/10.1063/5.0077646  

   Asch, Anna; J. Brady, Ethan; Gallardo, Hugo; Hood, John; Chu, Bryan; Farazmand, Mohammad (April 2022, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data are obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead, we only use a small subset of observable quantities, which can be measured in practice. We investigate the feasibility of this model-assisted framework on three different dynamical systems (Rössler attractor, FitzHugh–Nagumo model, and a turbulent fluid flow) and three different deep neural network architectures (feedforward, long short-term memory, and reservoir computing). In each case, we study the prediction accuracy, robustness to noise, reproducibility under repeated training, and sensitivity to the type of input data. In particular, we find long short-term memory networks to be most robust to noise and to yield relatively accurate predictions, while requiring minimal fine-tuning of the hyperparameters. 

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3. Responses of Coastal Ecosystems to Climate Change: Insights from Long-Term Ecological Research

   https://doi.org/10.1093/biosci/biac006  

   Reed, Daniel C; Schmitt, Russell J; Burd, Adrian B; Burkepile, Deron E; Kominoski, John S; McGlathery, Karen J; Miller, Robert J; Morris, James T; Zinnert, Julie C (August 2022, BioScience)

   abstract Coastal ecosystems play a disproportionately large role in society, and climate change is altering their ecological structure and function, as well as their highly valued goods and services. In the present article, we review the results from decade-scale research on coastal ecosystems shaped by foundation species (e.g., coral reefs, kelp forests, coastal marshes, seagrass meadows, mangrove forests, barrier islands) to show how climate change is altering their ecological attributes and services. We demonstrate the value of site-based, long-term studies for quantifying the resilience of coastal systems to climate forcing, identifying thresholds that cause shifts in ecological state, and investigating the capacity of coastal ecosystems to adapt to climate change and the biological mechanisms that underlie it. We draw extensively from research conducted at coastal ecosystems studied by the US Long Term Ecological Research Network, where long-term, spatially extensive observational data are coupled with shorter-term mechanistic studies to understand the ecological consequences of climate change. 

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4. Learning molecular dynamics with simple language model built upon long short-term memory neural network

   https://doi.org/10.1038/s41467-020-18959-8  

   Tsai, Sun-Ting; Kuo, En-Jui; Tiwary, Pratyush (December 2020, Nature Communications)

   null (Ed.)

   Abstract Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model’s reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems. 

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5. LATTE: L STM Self- Att ention based Anomaly Detection in E mbedded Automotive Platforms

   https://doi.org/10.1145/3476998  

   Kukkala, Vipin Kumar; Thiruloga, Sooryaa Vignesh; Pasricha, Sudeep (October 2021, ACM Transactions on Embedded Computing Systems)

   Modern vehicles can be thought of as complex distributed embedded systems that run a variety of automotive applications with real-time constraints. Recent advances in the automotive industry towards greater autonomy are driving vehicles to be increasingly connected with various external systems (e.g., roadside beacons, other vehicles), which makes emerging vehicles highly vulnerable to cyber-attacks. Additionally, the increased complexity of automotive applications and the in-vehicle networks results in poor attack visibility, which makes detecting such attacks particularly challenging in automotive systems. In this work, we present a novel anomaly detection framework called LATTE to detect cyber-attacks in Controller Area Network (CAN) based networks within automotive platforms. Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time. Subsequently, a novel detection scheme (also trained at design time) is used to detect various cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE framework under different automotive attack scenarios and present a detailed comparison with the best-known prior works in this area, to demonstrate the potential of our approach. 

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