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1. Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model

   https://doi.org/10.1073/pnas.2409257121  

   Wu, Fan; Huang, Yan; Yang, Guokun; Ye, Sheng; Mukamel, Shaul; Jiang, Jun (July 2024, Proceedings of the National Academy of Sciences)

   Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a “spectrum-structure” correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments. 

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2. Dynamic-GAN: Learning Spatial-Temporal Attention for Dynamic Object Removal in Feature Dense Environments

   https://doi.org/10.1109/IROS47612.2022.9981102  

   Trombley, Christopher M; Kumar Das, Sumit; Popa, Dan O (October 2022, Dynamic-GAN: Learning Spatial-Temporal Attention for Dynamic Object Removal in Feature Dense Environments)

   This paper presents an attention-based, deep learning framework that converts robot camera frames with dynamic content into static frames to more easily apply simultaneous localization and mapping (SLAM) algorithms. The vast majority of SLAM methods have difficulty in the presence of dynamic objects appearing in the environment and occluding the area being captured by the camera. Despite past attempts to deal with dynamic objects, challenges remain to reconstruct large, occluded areas with complex backgrounds. Our proposed Dynamic-GAN framework employs a generative adversarial network to remove dynamic objects from a scene and inpaint a static image free of dynamic objects. The Dynamic-GAN framework utilizes spatial-temporal transformers, and a novel spatial-temporal loss function. The evaluation of Dynamic-GAN was comprehensively conducted both quantitatively and qualitatively by testing it on benchmark datasets, and on a mobile robot in indoor navigation environments. As people appeared dynamically in close proximity to the robot, results showed that large, feature-rich occluded areas can be accurately reconstructed with our attention-based deep learning framework for dynamic object removal. Through experiments we demonstrate that our proposed algorithm has up to 25% better performance on average as compared to the standard benchmark algorithms. 

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3. A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks

   https://doi.org/10.1109/TBDATA.2018.2870136  

   Srinivasan, Sriram; Pollard, Samuel; Das, Sajal K.; Norris, Boyana; Bhowmick, Sanjukta (September 2018, IEEE Transactions on Big Data)

   In this paper, we present a network-based template for analyzing large-scale dynamic data. Specifically, we present a novel shared-memory parallel algorithm for updating treebased structures, including connected components (CC) and the minimum spanning tree (MST) on dynamic networks. We propose a rooted tree-based data structure to store the edges that are most relevant to the analysis. Our algorithm is based on updating the information stored in this rooted tree.In this paper, we present a network-based template for analyzing large-scale dynamic data. Specifically, we present a novel shared-memory parallel algorithm for updating tree-based structures, including connected components (CC) and the minimum spanning tree (MST) on dynamic networks. We propose a rooted tree-based data structure to store the edges that are most relevant to the analysis. Our algorithm is based on updating the information stored in this rooted tree. 

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4. A Convex Optimization-based Dynamic Model Identification Package for the da Vinci Research Kit

   https://doi.org/10.1109/LRA.2019.2927947  

   Wang, Yan; Gondokaryono, Radian; Munawar, Adnan; Fischer, Gregory Scott (July 2019, IEEE Robotics and Automation Letters)

   The da Vinci Research Kit (dVRK) is a teleoperated surgical robotic system. For dynamic simulations and model-based control, the dynamic model of the dVRK is required. We present an open-source dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK. A convex optimization-based method is used to identify the dynamic parameters of the dVRK subject to physical consistency. Experimental results show the effectiveness of the modeling and the robustness of the package. Although this software package is originally developed for the dVRK, it is feasible to apply it on other similar robots. 

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5. A Derivative-Free Observability Analysis Method of Stochastic Power Systems

   https://doi.org/10.1109/PESGM46819.2021.9637959  

   Zheng, Zongsheng; Xu, Yijun; Mili, Lamine; Liu, Zhigang; Korkali, Mert; Wang, Yuhong (July 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   The observability analysis of a time-varying nonlinear dynamic model has recently attracted the attention of power engineers due to its vital role in power system dynamic state estimation. Generally speaking, due to the nonlinearity of the power system dynamic model, the traditional derivative-based observability analysis approaches either rely on the linear approximation to simplify the problem or require a complicated derivation procedure that ignores the uncertainties of the dynamic system model and of the observations represented by stochastic noises. Facing this challenge, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only brings a low complexity, but also enables us to quantify the degree of observability by considering the stochastic nature of the dynamic systems. The excellent performances of the proposed method is demonstrated using simulations of a decentralized dynamic state estimation performed on a power system using a synchronous generator model with IEEE-DC1A exciter and a TGOV1 turbine-governor. 

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