This work aims to address the problem of long-term dynamic forecasting in complex environments where data are noisy and irregularly sampled. While recent studies have introduced some methods to improve prediction performance, these approaches still face a significant challenge in handling long-term extrapolation tasks under such complex scenarios. To overcome this challenge, we propose Phy-SSM, a general-purpose framework that integrates partial physics knowledge into state space models (SSMs) for long-term dynamics forecasting in complex environments. Our motivation is that SSMs can effectively capture long-range dependencies in sequential data and model continuous dynamical systems, while the incorporation of physics knowledge improves generalization ability. The key challenge lies in how to seamlessly incorporate partially known physics into SSMs. To achieve this, we decompose partially known system dynamics into known and unknown state matrices, which are integrated into a Phy-SSM unit. To further enhance long-term prediction performance, we introduce a physics state regularization term to make the estimated latent states align with system dynamics. Besides, we theoretically analyze the uniqueness of the solutions for our method. Extensive experiments on three real-world applications, including vehicle motion prediction, drone state prediction, and COVID-19 epidemiology forecasting, demonstrate the superior performance of Phy-SSM over the baselines in both long-term interpolation and extrapolation tasks. The source code will be publicly available upon publication.

Abstract This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications; however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development.

In biochemical modeling, some foundational systems can exhibit sudden and profound behavioral shifts, such as the cellular signaling pathway models, in which the physiological responses promptly react to environmental changes, resulting in steep changes in their dynamic model trajectories. These steep changes are one of the major challenges in biochemical modeling governed by nonlinear differential equations. One promising way to tackle this challenge is converting the input data from the time domain to the frequency domain through Fourier Neural Operators, which enhances the ability to analyze data periodicity and regularity. However, the effectiveness of these Fourier based methods diminishes in scenarios with complex abrupt switches. To address this limitation, an innovative Multiscale Attention Wavelet Neural Operator (MAWNO) method is proposed in this paper, which comprehensively combines the attention mechanism with the versatile wavelet transforms to effectively capture these abrupt switches. Specifically, the wavelet transform scrutinizes data across multiple scales to extract the characteristics of abrupt signals into wavelet coefficients, while the self-attention mechanism is adeptly introduced to enhance the wavelet coefficients in high-frequency signals that can better characterize the abrupt switches. Experimental results substantiate MAWNO’s supremacy in terms of accuracy on three classical biochemical models featuring periodic and steep trajectories. https://github.com/SUDERS/MAWNO.

AMP-activated protein kinase (AMPK), a cellular energy sensor in response to changes in the ADP/ATP ratio, has been proven to not only play roles in metabolic actions but also holds pivotal roles in neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease, and Lewy body dementia. However, the downstream effectors regulated by AMPK and its specific contributions to the pathology of these diseases remain elusive. In this study, we combine the power of systems biology and neuroscience to understand the dynamics and regulatory effects of AMPK under the progression of AD. Particularly, our study develops a regulatory protein network that seeks to demystify this intricate protein-protein interaction, placing particular emphasis on the role of AMPK in the pathology of AD. By focusing on two important cellular activities, mRNA translation and autophagy, our model explores the underlying effects of the central governance of a change in AMPK activity and concludes that eEF2-controlled translation is more sensitive to the perturbation. Additionally, we adjust various kinetic parameters to restore proteins affected by the disease to their baseline levels, with the goal of identifying potential new drug targets. The model accurately captures the regulatory effect of AMPK, provides the temporal dynamics of key regulators in AD progression, contributes to the understanding of the disease’s pathophysiology, and potentially generalizes the function of AMPK into other neurodegenerative diseases.

Mobile app ecosystem has gained giant success in providing services on mobile devices to facilitate almost all aspects in our daily life. However, the whole-package installation and dramatically increasing package size are now preventing users from trying more apps. To address the issue, many lightweight frameworks have emerged, enabling to provide the experience of instant service consumption where apps are of small size and no installation is needed to consuming services provided by the apps. In this paper, we conduct the first empirical study on instant service consumption on mobile devices. We focus on one of the most popular frameworks, quick apps, which are proposed and supported by nine mainstream mobile phone manufacturers in China. Quick apps are implemented with Web-based technologies, and run as native apps without the need of installation. We find that quick apps have much smaller size and only provide a limited set of services compared to their corresponding native apps. Then, we characterize the performance differences between quick apps and native apps in terms of launching time, data drain, and network connections, when the two kinds of apps provide the same services. Our observations reveal that quick apps perform better than native apps thanks to its much smaller size and less functionalities in a single page. Finally, we propose a machine learning based approach to helping developers construct the quick app from an existing native app.