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Advancing brain-inspired computing with hybrid neural networks

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National Science Review 2024年第5期11卷 56-71页

作者： Faqiang Liu Hao Zheng Songchen Ma Weihao Zhang Xue Liu Yansong Chua Luping Shi Rong Zhao Center for Brain-Inspired computing Research Optical Memory National Engineering Research CenterTsinghua University-China Electronics Technology HIK Group Co.Joint Research Center for Brain-inspired ComputingIDGMc Govern Institute for Brain Research Department of Precision Instrument Tsinghua University Neuromorphic computing Laboratory China Nanhu Academy of Electronics and Information Technology

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network(HNN), which integrates computer-science-oriented artificial neural networks(ANNs) with neuroscience-oriented spiking neural networks(SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

关键词： brain-inspired computing hybrid neural network dual-brain driven multi-network integration neuromorphic system

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

Metal-based inorganic nanocrystals for biological sonodynamic therapy applications:recent progress and perspectives

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Rare Metals 2024年第2期43卷 413-430页

作者： Dong Wang Jia-Tao Zhang institute of Medical-Industrial Integration Beijing Key Laboratory of Structurally Controllable Advanced Functional Materials and Green ApplicationsSchool of Materials Science and EngineeringBeijing Institute of TechnologyBeijing100081China Key Laboratory of Medical molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologySchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing100081China

With the fast development of technology for the treatment of tumor and bacteria,photo-therapeutic strate-gies emerge as a kind of highly effective and common treatment,but the low tissue penetration depth of light limits their *** therapy(SDT),as an efficient and non-invasive treatment,attracts more people's attention due to the inherent property of high tissue *** soft tissue penetration depth of ultrasound(US)can even reach more than 10 cm,which has great advantage over that of ***,many sonosensitizers are studied and applied to SDT-based ***-based inorganic nanocrystals are able to generate more reactive oxygen species(ROS)due to the special composition and band *** representative achievements and the specific functions of the nanocrystals sonosensitizers are summarized in this work,and the relationship of structure/composition-SDT performance and the internally regulated composite is ***-gistic effects of SDT in combination with other therapeutic modalities are mainly *** the same time,the critical and potential issues and future perspectives are addressed.

关键词： Metal-based inorganic nanocrystals Sonodynamic therapy(SDT) Tumor treatment Antibacteria

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Nanophononic metamaterials induced proximity effect in heat flux regulation

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Frontiers of physics 2024年第2期19卷 149-156页

作者： Jian Zhang Haochun Zhang Gang Zhang School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China institute of High Performance computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recent studies have shown that the construction of nanophononic metamaterials can reduce thermal conductivity without affecting electrical properties,making them promising in many fields of application,such as energy conversion and thermal ***,although extensive studies have been carried out on thermal conductivity reduction in nanophononic metamaterials,the local heat flux characteristic is still *** this work,we construct a heat flux regulator which includes a silicon nanofilm with silicon *** regulator has remarkable heat flux regulation ability,and various impacts on the regulation ability are ***,even in the region without nanopillars,the local heat current is still lower than that in pristine silicon nanofilms,reduced by the neighboring nanopillars through the thermal proximity *** combine the analysis of the phonon participation ratio with the intensity of localized phonon modes to provide a clear *** findings not only provide insights into the mechanisms of heat flux regulation by nanophononic metamaterials,but also will open up new research directions to control local heat flux for a broad range of applications,including heat management,thermoelectric energy conversion,thermal cloak,and thermal concentrator.

关键词： nanophononic metamaterials proximity effect

Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

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Machine Intelligence Research 2024年第2期21卷 257-271页

作者： Bo-Jing Feng Xi Cheng Hao-Nan Xu Wen-Fang Xue Center for Research on Intelligent Perception and computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial *** models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating ***,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between *** addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial *** experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

关键词： Corporate credit rating hierarchical relation heterogeneous graph neural networks adversarial learning

Implementation and validation of a bonded particle model to predict rheological properties of viscoelastic materials

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Particuology 2024年第6期89卷 198-210页

作者： Michael Mascara Arno Mayrhofer Stefan Radl Christoph Kloss institute of Process and Particle Engineering Graz University of TechnologyInffeldgasse 13Graz8010Austria DCS computing GmbH Industriezeile 35Linz4020Austria

This work focuses on implementing a particle-based method able to characterize viscoelastic materials whose rheological properties,such as storage modulus G′and loss modulus G″,are *** is based on the bonded particle model,with the elastic constitutive relation here substituted with a viscoelastic one to capture time-scale *** Burgers model,vastly used in literature to model viscoelastic systems,is discretized and *** test case used for calibration comprises of a cubic lattice,sheared with a periodic motion,to mimic the effect of a shear *** appropriate filtering of the stress response,the rheological properties are obtained,highlighting the effect of the lattice geometry,as well as the particle size,on the accuracy of the ***,the Burgers parameters are calibrated by analytically fitting the experimental dataset,showing the limitation of the Burgers *** micro-contact parameters are obtained from the macro parameters through appropriate *** completing a frequency sweep,the simulated G′and G″show a relatively large error,around 25%for G’for *** this reason,a more robust model,namely the generalized Maxwell model,has been *** calibration procedure is performed in the same fashion as for the Burgers ***,the tangential micro-contact parameters are scaled *** normal *** scaling parameter,calledα,is calibrated by minimizing the root mean square error between simulation and experimental data,giving errors below 10%in both G′and G″for a large ***,a full ring plate-plate rheometer setup is simulated,and the simulation is compared with the given experimental dataset,again finding a good agreement.

关键词： DEM Rheology Numerical modeling Viscoelasticity

Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

Quantum search with prior knowledge

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Science China(Information Sciences) 2024年第9期67卷 321-329页

作者： Xiaoyu HE Xiaoming SUN Jialing ZHANG State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example.

关键词： quantum computing quantum search quantum query algorithm prior knowledge

Interaction of edge dislocations with voids in tungsten

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Tungsten 2024年第3期6卷 633-646页

作者： Arseny Kazakov Rita I.Babicheva Aleksandr Zinovev Dmitry Terentyev Kun Zhou Elena A.Korznikova Sergey V.Dmitriev Research Laboratory for Metals and Alloys under Extreme Impacts Ufa University of Science and Technology institute for Metals Superplasticity Problems of RAS School of Mechanical and Aerospace Engineering Nanyang Technological University institute for Nuclear Energy Technology Belgian Nuclear Research Center Technological Machines and Equipment Department Ufa State Petroleum Technological University institute of Mechanical Science Problems of RAS institute of molecule and Crystal Physics UFRC of Russian Academy of Sciences

Tungsten is widely used as a material capable of withstanding extreme operating conditions and is one of the candidates for use in fusion and Generation Ⅳ fission *** modelling implemented to solve various material science problems is an indispensable approach for improving material *** work analyzes the interaction of an edge dislocation with a void in single-crystal tungsten with the help of molecular dynamics *** different approaches for studying the dynamics of dislocations in tungsten that differ in boundary conditions are *** interactions of the dislocation and void at different temperatures are investigated,and based on the obtained stress-strain curves,the dislocation obstacle strength for voids having various diameters is *** is shown that when applied to a crystal with small voids,the model with the fixed boundary conditions returns incorrect ***,the influence of the cell size for the model with periodic boundary conditions is analyzed,and the case of a non-central interaction of a dislocation with a void is *** obtained results improve our understanding of the mechanical response of irradiated materials.

关键词： Tungsten Molecular dynamics method Dislocations Voids Boundary conditions