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Artificial intelligence in cancer target identification and drug discovery

Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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Microgrid Frequency Stability: A Proactive Scheme Based on Dynamic Predictions

Microgrid Frequency Stability: A Proactive Scheme Based on D...

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Innovative Smart Grid Technologies (ISGT)

作者： Gian Paramo Arturo Bretas Sean Meyn Department of Electrical and Computer Engineering University of Florida Gainesville FL G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Distributed Systems Group Pacific Northwest National Laboratory Richland USA

The dynamic nature of microgrids introduces challenges in the context of frequency stability. This work presents a framework where the future state of microgrid frequency is predicted and corrective actions are optimized. Predictions are generated through Bayesian filters leveraging synchronized data acquired via PMUs. Taking a proactive approach makes it possible to optimize corrective actions considering dynamic system conditions. Testing is conducted via Matlab simulations. The performance of the solution presented in this work is compared to traditional load-shedding schemes and to predictive solutions found in literature. The results indicate that the proposed framework outperforms both. Some of the advantages of this framework include a reduction in amount of load dropped during compensation, the use of adaptive parameters which eliminates the need to simulate contingency conditions, and dynamic uncertainty quantification provided the particle filter.

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Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Communication Network Layer State Estimation Measurement Mod...

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Innovative Smart Grid Technologies (ISGT)

作者： Reynold Mathieu Sharon Boamah Austin Cooper Dennis Agnew Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Pacific Northwest National Laboratory Richland WA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in the generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing an enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects of th

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Chao Gao Mengting Bai Xiaoqin Lian Xiangyu Hu Shuangshuang Han Shichao Chen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios.

关键词： Computational modeling Semantics Feature extraction Data models Neck Social intelligence Quality assessment Computational complexity Defect detection Load modeling

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SyNet: A Synergistic Network for 3D Object Detection through Geometric-Semantic-based Multi-Interaction Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Xiaoqin Bi, Kenan Chan, Sixian Lu, Shijian Zhou, Xiaolong Wenzhou University Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou325035 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei230031 China Nanyang Technological University School of Computer Science and Engineering China Quzhou University The College of Electrical and Information Engineering Quzhou324000 China

Driven by rising demands in autonomous driving, robotics, etc., 3D object detection has recently achieved great advancement by fusing optical images and LiDAR point data. On the other hand, most existing optical-LiDAR fusion methods straightly overlay RGB images and point clouds without adequately exploiting the synergy between them, leading to suboptimal fusion and 3D detection performance. Additionally, they often suffer from limited localization accuracy without proper balancing of global and local object information. To address this issue, we design a synergistic network (SyNet) that fuses geometric information, semantic information, as well as global and local information of objects for robust and accurate 3D detection. The SyNet captures synergies between optical images and LiDAR point clouds from three perspectives. The first is geometric, which derives high-quality depth by projecting point clouds onto multi-view images, enriching optical RGB images with 3D spatial information for a more accurate interpretation of image semantics. The second is semantic, which voxelizes point clouds and establishes correspondences between the derived voxels and image pixels, enriching 3D point clouds with semantic information for more accurate 3D detection. The third is balancing local and global object information, which introduces deformable self-attention and cross-attention to process the two types of complementary information in parallel for more accurate object localization. Extensive experiments show that SyNet achieves 70.7% mAP and 73.5% NDS on the nuScenes test set, demonstrating its effectiveness and superiority as compared with the state-of-the-art. © 2025 IEEE.

关键词： Image fusion

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Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

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IEEE Geoscience and Remote Sensing Letters 2023年 20卷 1页

作者： Zhang, Haopeng Lin, Zijing Gao, Feng Dong, Junyu Du, Qian Li, Heng-Chao Ocean University of China School of Computer Science and Technology Qingdao266100 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States Southwest Jiaotong University Sichuan Provincial Key Laboratory of Information Coding and Transmission Chengdu610031 China

Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attention in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate for the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the nonlinear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://***/summitgao/CAMixer. © 2004-2012 IEEE.

关键词： Feature extraction

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Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography 10

Ultrafast CMOS image sensors and data-enabled super-resoluti...

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10th International Workshop on Semiconductor Pixel Detectors for Particles and Imaging, Pixel 2022

作者： Yue, Xin Lin, Shanny Li, Wenting Wolfe, Bradley T. Clayton, Steven Makela, Mark Morris, C.L. Spannagel, Simon Ramberg, Erik Estrada, Juan Zhu, Hao Liu, Jifeng Fossum, Eric R. Wang, Zhehui Thayer School of Engineering at Dartmouth Dartmouth College HanoverNH03755 United States Los Alamos National Laboratory Los AlamosNM87545 United States Chandra Family Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Fermi National Accelerator Laboratory BataviaIL60510 United States

We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus 'super-resolution'). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Charge coupled devices

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High-speed and low-power molecular dynamics processing unit(MDPU)with ab initio accuracy

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npj Computational Materials 2024年第1期10卷 559-568页

作者： Pinghui Mo Yujia Zhang Zhuoying Zhao Hanhan Sun Junhua Li Dawei Guan Xi Ding Xin Zhang Bo Chen Mengchao Shi Duo Zhang Denghui Lu Yinan Wang Jianxing Huang Fei Liu Xinyu Li Mohan Chen Jun Cheng Bin Liang Weinan E Jiayu Dai Linfeng Zhang Han Wang Jie Liu College of Integrated Circuits Hunan UniversityChangsha410082P.R.China College of Electrical and Information Engineering Hunan UniversityChangsha410082P.R.China College of Computer National University of Defense TechnologyChangsha410073P.R.China Department of Physics National University of Defense TechnologyChangsha410073P.R.China DP Technology 100080 BeijingP.R.China AI for Science Institute 100080 BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking University100871 BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking University100871 BeijingP.R.China School of Mathematical Science Peking University100871 BeijingP.R.China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEMCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen361005P.R.China School of Integrated Circuits Peking University100871 BeijingP.R.China Center for Machine Learning Research Peking University100871 BeijingP.R.China Institute of Applied Physics and Computational Mathematics 100088 BeijingP.R.China Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou511300P.R.China Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various *** the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”***,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and *** solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio *** significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.

关键词： consuming power MDP

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Low Earth Orbit Satellite Networks: Architecture, Key Technologies, Measurement, and Open Issues

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IEEE Network 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhao, Jinwei Yin, Zhisheng Cheng, Nan Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University School of Cyber Science and Engineering China Xidian University School of Cyber Engineering China University of Victoria Department of Computer Science Canada Xidian University State Key Laboratory of ISN School of Telecommunications Engineering China

Low Earth Orbit (LEO) satellite networks are transforming global connectivity by enabling high-speed, low-latency Internet access. Particularly, they significantly facilitate areas where terrestrial networks are not deployed or destroyed. Meanwhile, LEO satellite technology is experiencing an unprecedented surge in development. This paper provides a comprehensive and up-to-date overview of LEO satellite networks. First, the evolution of LEO satellites is introduced, followed by an exploration of the components and communication architecture within LEO satellite networks using representative examples. Second, key technologies, including routing, handover management, and digital twins, are summarized, and some practical application scenarios are discussed. The performance of LEO satellite networks, illustrated by SpaceX’s Starlink, is then evaluated to understand its scheduling algorithm and network characteristics, which can inform future satellite-related algorithms and architecture design. Finally, as LEO constellations continue to expand, practical operations face significant challenges in management, technology, and security. Consequently, we highlight some open research issues to provide potential inspiration for academia and industry in satellite networking. © 1986-2012 IEEE.

关键词： Geodetic satellites

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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