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A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

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Protein & Cell 2016年第1期7卷 46-62页

作者： Hong Shan Zihao wang Fa Zhang Yong Xiong Chang-Cheng Yin Fei Sun Department of Biophysics College of Basic Medical Sciences Peking University Health Science Center Beijing 100191China National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06511 USA Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large （60S/50S） and the small （40S/30S） subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.

关键词： cryo-electron microscopy single particleanalysis conformational heterogeneity rigid module Iocaoptimization refinement

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SPDiffusion: Semantic Protection Diffusion Models for Multi-concept Text-to-image Generation

arXiv

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arXiv 2024年

作者： Zhang, Yang Zhang, Rui Nie, Xuecheng Li, Haochen Chen, Jikun Hao, Yifan Zhang, Xin Liu, Luoqi Li, Ling State Key Lab of Processors Institute of Computing Technology CAS China MT Lab Meitu Inc China Intelligent Software Research Center Institute of Software CAS China University of Chinese Academy of Sciences China

Recent text-to-image models have achieved impressive results in generating high-quality images. However, when tasked with multi-concept generation creating images that contain multiple characters or objects, existing methods often suffer from semantic entanglement, including concept entanglement and improper attribute binding, leading to significant text-image inconsistency. We identify that semantic entanglement arises when certain regions of the latent features attend to incorrect concept and attribute tokens. In this work, we propose the Semantic Protection Diffusion Model (SPDiffusion) to address both concept entanglement and improper attribute binding using only a text prompt as input. The SPDiffusion framework introduces a novel concept region extraction method SP-Extraction to resolve region entanglement in cross-attention, along with SP-Attn, which protects concept regions from the influence of irrelevant attributes and concepts. To evaluate our method, we test it on existing benchmarks, where SPDiffusion achieves state-of-the-art results, demonstrating its effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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AutoOS: make your OS more powerful by exploiting large language models 24

AutoOS: make your OS more powerful by exploiting large langu...

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Proceedings of the 41st International Conference on Machine Learning

作者： Huilai Chen Yuanbo Wen Limin Cheng Shouxu Kuang Yumeng Liu Weijia Li Ling Li Rui Zhang Xinkai Song Wei Li Qi Guo Yunji Chen State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China Intelligent Software Research Center Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China

With the rapid development of Artificial Intelligence of Things (AIoT), customizing and optimizing operating system (OS) kernel configurations for various AIoT application scenarios is crucial for maximizing system performance. However, existing approaches falter due to the overwhelming problem complexity (i.e., over 15,000 configuration options in the Linux kernel), together with the huge evaluation costs and error-prone options that may result in OS boot-up failure, which all make it an unresolved problem to optimize the Linux kernel automatically. In this paper, we introduce AutoOS, a novel framework exploiting Large Language Models for customizing and optimizing OS kernel configurations automatically for various AIoT application scenarios. Inspired by the inherently directory-structured kernel configuration process, we first formulate our research problem as optimizing on a dynamic tree. We then propose a novel framework integrating a state machine-based traversal algorithm as the observe-prune-propose-act-correct loop, which can effectively refine the optimization space and ensure a successful OS boot-up. Experimental results show that AutoOS can automatically customize and optimize the OS kernel configurations without human effort. More importantly, AutoOS even achieves better performance by up to 25% than vendor-provided configuration.

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Novel Detection Service Method for Moving Object in IOT

Novel Detection Service Method for Moving Object in IOT

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2016 3rd International Conference on Materials Engineering,Manufacturing technology and Control(ICMEMTC 2016)

作者： Jie Chen Degan Zhang School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education

In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection service method through mixing Gaussian Mixture Model(GMM), edge detection service method and continuous frame difference method in this paper. In time domain, the new method uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection service method which mixes edge detection service method, continuous frame difference method and GMM to get initial contour of moving object, and gets ultimate moving object. This method not only can well adapt to the illumination gradients and background disturbance occurred on scene, but also can well solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional method. As experimental result showing, this method holds better real-time and robustness. It is not only easily implemented, but also can accurately detect moving object.

关键词： GMM embedded IOT moving object detection service

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Optimization for reliable erasure-coded storage allocation under multiple constraints

Optimization for reliable erasure-coded storage allocation u...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Guangping Xu Xing Guo Hua Zhang Sheng Lin Chunxia Yang Computer and Communication Engineering School Tianjin University of Technology Tianjin China Tianjin Key Lab. of Intelligence Computing and New Software Technology Tianjin China

ISBN: (纸本)9781479932153

To maximize the reliability of erasure coded objects in cloud storage, the optimal allocation is a challenging problem constrained with the node heterogeneities and erasure coding budgets. We model the optimal problem from a combinatorial view under multiple constraints and propose an efficient search algorithm to find the most reliable allocation. The experiments are evaluated and analyzed including the optimal reliability, redundancy reduction and allocation pattern.

关键词： Peer-to-peer computing Resource management Encoding Redundancy Availability Bandwidth

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Novel Approach of Vehicular Cooperative Communication Based on Strategy of Interval Type-2 Fuzzy Logic and Cooperative Game

IEEE Transactions on Sustainable Computing

引用

IEEE Transactions on Sustainable computing 2024年

作者： Zhang, Jie Zhang, Zhihao Zhang, Degan Ni, Chenhao Zhang, Ting Jiang, Xingru University of Macau E11 Key Lab of Internet of Things for Smart City Faculty of Science &technology China Tianjin University of Technology Tianjin Key Lab of Intelligent Computing & Novel software Technology 300384 China Tianjin University of Sport School of Sports Economics and Management Tianjin301617 China Beihang University College of Software Beijing100083 China

As an important branch of the Internet of Things (IoT), vehicular networks play a crucial role in the construction of intelligent transportation systems. However, due to the rapid movement of vehicles and signal obstruction, achieving high- quality and low-latency communication in vehicular networks remains a significant challenge. To address this issue, this paper proposes a novel data communication method based on interval type-2 fuzzy logic and cooperative game theory. Firstly, interval type-2 fuzzy logic is utilized to infer vehicle stability, thereby selecting high-quality backbone nodes. Concurrently, the memory and forgetfulness functions of the Gated Recurrent Unit are employed to retain critical data packets. Subsequently, a greedy algorithm and cooperative game theory model are used to describe the behavior of vehicles in Roadside-to-Vehicle (R2V) communication and Vehicle-to- Vehicle (V2V) communication, respectively. This approach encourages backbone nodes to cooperate and serve other vehicles based on a benefit function. Experimental results demonstrate that the proposed method excels in terms of transmission delay, coverage range, and data packet delivery success rate. © 2016 IEEE.

关键词： Kalman filters

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Context shift reduction for offline meta-reinforcement learning 23

Context shift reduction for offline meta-reinforcement learn...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yunkai Gao Rui Zhang Jiaming Guo Fan Wu Qi Yi Shaohui Peng Siming Lan Ruizhi Chen Zidong Du Xing Hu Qi Guo Ling Li Yunji Chen University of Science and Technology of China USTC Hefei China and State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Cambricon Technologies State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Cambricon Technologies and University of Chinese Academy of Sciences UCAS Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Shanghai Innovation Center for Processor Technologies SHIC Shanghai China University of Chinese Academy of Sciences UCAS Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences UCAS Beijing China

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem arises due to the distribution discrepancy between the contexts used for training (from the behavior policy) and testing (from the exploration policy). The context shift problem leads to incorrect task inference and further deteriorates the generalization ability of the meta-policy. Existing OMRL methods either overlook this problem or attempt to mitigate it with additional information. In this paper, we propose a novel approach called Context Shift Reduction for OMRL (CSRO) to address the context shift problem with only offline datasets. The key insight of CSRO is to minimize the influence of policy in context during both the meta-training and meta-test phases. During meta-training, we design a max-min mutual information representation learning mechanism to diminish the impact of the behavior policy on task representation. In the meta-test phase, we introduce the non-prior context collection strategy to reduce the effect of the exploration policy. Experimental results demonstrate that CSRO significantly reduces the context shift and improves the generalization ability, surpassing previous methods across various challenging domains.

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Contrastive modules with temporal attention for multi-task reinforcement learning 23

Contrastive modules with temporal attention for multi-task r...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Siming Lan Rui Zhang Qi Yi Jiaming Guo Shaohui Peng Yunkai Gao Fan Wu Ruizhi Chen Zidong Du Xing Hu Xishan Zhang Ling Li Yunji Chen University of Science and Technology of China and State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Cambricon Technologies State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Cambricon Technologies and University of Chinese Academy of Sciences UCAS Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Shanghai Innovation Center for Processor Technologies SHIC Shanghai China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and Cambricon Technologies University of Chinese Academy of Sciences UCAS Beijing China and Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences UCAS Beijing China

In the field of multi-task reinforcement learning, the modular principle, which involves specializing functionalities into different modules and combining them appropriately, has been widely adopted as a promising approach to prevent the negative transfer problem that performance degradation due to conflicts between tasks. However, most of the existing multi-task RL methods only combine shared modules at the task level, ignoring that there may be conflicts within the task. In addition, these methods do not take into account that without constraints, some modules may learn similar functions, resulting in restricting the model's expressiveness and generalization capability of modular methods. In this paper, we propose the Contrastive Modules with Temporal Attention(CMTA) method to address these limitations. CMTA constrains the modules to be different from each other by contrastive learning and combining shared modules at a finer granularity than the task level with temporal attention, alleviating the negative transfer within the task and improving the generalization ability and the performance for multi-task RL. We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks. Experimental results show that CMTA outperforms learning each task individually for the first time and achieves substantial performance improvements over the baselines. Our code can be found at https://***/niiceMing/CMTA.

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