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Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing

Journal of Materiomics 2023年第4期9卷 787-797页

作者： Yan Kang Yabo Chen Yinlong Tan Hao Hao Cheng Li Xiangnan Xie Weihong Hua Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha410073China

Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and *** by the activation of silent synapses via receptor insertion in neural synapses,we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered a-MoO_(3).Sn intercalation is capable of switching on the response of layered a-MoO_(3)to the stimuli of visible and near infrared light by decreasing the *** mimics the receptor insertion process in silent neural *** Sn-intercalated MoO_(3)(Sn-MoO_(3))exhibits persistent photoconductivity due to the donor impurity induced by Sn *** enables the two-terminal Sn-MoO_(3)device promising optoelectronic synapse with an ultrahigh paired pulse facilitation(PPF)up to 199.5%.On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic *** performance of the extensible artificial neural network(ANN)based on the Sn-MoO_(3)synapses are demonstrated in pattern ***,the recognition accuracy increases from 89.7%to 94.8%by activating more nodes into the *** is consistent with the recognition process of physical neural network during brain *** intercalation engineering of MoO_(3)may provide inspirations for the design of high-performance neuromorphic computing architectures.

关键词： Activation of silent synapse Intercalation Layered materials Neuromorphic computing

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A large scale parallel fluid-structure interaction computing platform for simulating structural responses to a detonation shock

A large scale parallel fluid-structure interaction computing...

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作者： Zhang, Sen Guo, Xiao-Wei Li, Chao Liu, Yi Fan, Sijiang Zhao, Ran Yang, Canqun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Due to the intrinsic nature of multi-physics, it is prohibitively complex to design and implement a simulation software platform for study of structural responses to a detonation shock. In this article, a partitioned fluid-structure interaction computing platform is designed for parallel simulating structural responses to a detonation shock. The detonation and wave propagation are modeled in an open-source multi-component solver based on OpenFOAM and blastFoam, and the structural responses are simulated through the finite element library ***. To capture the interaction dynamics between the fluid and the structure, both solvers are adapted to preCICE. For improving the parallel performance of the computing platform, the inter-solver data is exchanged by peer-to-peer communications and the intermediate server in conventional multi-physics software is eliminated. Furthermore, the coupled solver with detonation support has been deployed on a computing cluster after considering the distributed data storage and load-balancing between solvers. The 3D numerical result of structural responses to a detonation shock is presented and analyzed. On 256 processor cores, the speedup ratio of the simulations for a detonation shock reach 178.0 with 5.1 million of mesh cells and the parallel efficiency achieve 69.5%. The results demonstrate good potential of massively parallel simulations. Overall, a general-purpose fluid-structure interaction software platform with detonation support is proposed by integrating open source codes. And this work has important practical significance for engineering application in fields of construction blasting, mining, and so forth. © 2021 John Wiley & Sons, Ltd.

关键词： Detonation

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OL-CBBA: An Online Task Allocation Algorithm under Weak Communication Conditions 29

OL-CBBA: An Online Task Allocation Algorithm under Weak Comm...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Jun Huang, Wanrong Wu, Honglin Cai, Zhongxuan Zhang, Yongjun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology Changsha China

ISBN: (纸本)9798350330717

Multiple unmanned aerial vehicles (UAVs) and multiple tasks allocation problem is difficult to solve. Existing task allocation algorithms assume that the UAVs' position is static, and cannot assign tasks with the changing UAVs' position simultaneously during task execution. Those algorithms reduce the efficiency of task allocation. In this paper, we propose an online consensus-based bundle algorithm (OL-CBBA) under weak communication for dynamic task allocation. We consider the situation that the location information of UAVs will constantly change during the dynamic execution of tasks. The algorithm first improves the static CBBA to an online algorithm by updating the task marginal score in task path. Moreover, we specify a flag for the convergence of individual tasks, allowing UAVs to start executing tasks earlier. Extensive comparative experiments prove the highly consistent efficiency of OL-CBBA under weak communication conditions. Specifically, the proposed OL-CBBA attains up to 22% improvement compared with CBBA. © 2023 IEEE.

关键词： Auction Algorithm Decentralized System Dynamic Task Allocation Weak Communication

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Degenerate polarization entangled photon source based on a single Ti-diffusion lithium niobate waveguide in a polarization Sagnac interferometer

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Chinese Physics B 2023年第8期32卷 69-73页

作者：孙宇孙昌伟周唯杨然端家晨龚彦晓徐平祝世宁 National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China NanZhi Institude of Advanced Optoelectronic Integration Nanjing 210018China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Combining a Ti-diffusion periodically poled lithium niobate(PPLN)waveguide with a Sagnac interferometer,two opposite directions type-II spontaneous parametric down conversions(SPDC)occur coherently and yield a high brightness,high stability polarization entanglement *** source produces degenerate photon pairs at 1540.4 nm with a brightness of B=(1.36±0.03)×10^(6) pairs/(s·nm·m W).We perform quantum state tomography to reconstruct the density matrix of the output state and obtain a fidelity of F=0.983±*** high brightness and phase stability of our waveguide source enable a wide range of quantum information experiments operating at a low pump power as well as hold the advantage in mass production which can promote the practical applications of quantum technologies.

关键词： polarization entanglement source Sagnac interferometer quantum information lithium niobate waveguide

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Towards Radar Emitter Recognition in Changing Environments with Domain Generalization 2

Towards Radar Emitter Recognition in Changing Environments w...

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2nd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2023

作者： Wu, Honglin Li, Xueqiong Lan, Long Xu, Liyang Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798400716430

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task. However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes. In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments. Specifically, we first design several noise generators to simulate varied scenes. Different from conventional augmentation methods, our introduced generators carefully enhance the diversity of the detected signals and meanwhile maintain the semantic features of the signals. Moreover, we propose a signal scene domain classifier that works in the manner of adversarial learning. The proposed classifier guarantees the signal predictor to generalize to different scenes. Extensive comparative experiments prove the proposed method's superiority. © 2023 ACM.

关键词： Electronic warfare

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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A blockchain-empowered authentication scheme for worm detection in wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 265-272页

作者： Yuling Chen Xiong Yang Tao Li Yi Ren Yangyang Long State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Computing Science University of East AngliaNorwichNR57TJUK

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and *** special structure of WSN brings both convenience and *** example,a malicious participant can launch attacks by capturing a physical ***,node authentication that can resist malicious attacks is very important to network ***,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for *** our scheme,all nodes are managed by utilizing the identity information stored on the ***,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection *** experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

关键词： Wireless Sensor Network(WSN) Node authentication Blockchain Tangle Worm detection

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Decomposition, Interaction, Reconstruction Meets Global Context Learning In Visual Tracking 48

Decomposition, Interaction, Reconstruction Meets Global Cont...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Huibin Hu, Kun Cao, Mingyu Wang, Mengzhu Xu, Liyang Yang, Wenjing Institute for Quantum Information State Key Laboratory of High Performance Computing China National University of Defense Technology College of Computer Science and Technology Changsha China

ISBN: (纸本)9781728163277

Tensor decomposition and reconstruction attention is a promising global context learning approach because it can remain efficient while avoiding feature compression. To exploit its potential even further in visual tracking, we redesign a 3D tensor modeling paradigm, namely tensor Decomposition, Interaction, Reconstruction attention (DIR), respectively corresponding to three function components, Tensor Decomposition Module (TDM), Tensor Interaction Module (TIM) and Context Reconstruction Module (CRM). Specifically, TDM decomposes a 3D tensor feature into rank-1 context fragments in different dimension views. The ingenuity here lies in the introduction of Circular Convolution for processing features at arbitrary scales and channel-sharing segments to enhance the interaction of the two branches in the Siamese network architecture. TIM obtains the tensor planes of each dimension by the Cross-Similarity operation of rank-1 tensors and fused cubic features, which brings more interactions between all feature dimensions. CRM reconstructs 3D context representations with the outputs of the above modules. In experiments, DIR is embedded into the tracker to verify its effectiveness. © 2023 IEEE.

关键词： context reconstruction Siamese tracking single object tracking tensor plane

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Integrating Velox into TinkerPop for Graph Queries on Vectorized Engine 3

Integrating Velox into TinkerPop for Graph Queries on Vector...

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3rd International Conference on Electronic Information Engineering and computer, EIECT 2023

作者： Li, Zihao Xu, Liyang Jin, Ruochun Chen, Huan Tang, Yuhua National University of Defense Technology Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Changsha410073 China

ISBN: (纸本)9798350357707

To enhance the query efficiency of relational databases and build a unified computing backend, Meta has developed Velox, a vectorized execution engine library based on columnar storage, Currently, there is no standardized specification for computation engine, and storage in graph databases, leading to failed to effectively utilize the vectorized processing capability of modern CPU. In this paper, we propose a middleware that primarily focuses on (1) non-invasively integrating Velox into the TinkerPop framework to provide unified vectorized engine acceleration for all graph databases supporting the TinkerPop specification;(2) conducting graph queries based on the relational data storage model, eliminating the overhead of transforming the storage model into a graph storage model;(3) validating the acceleration effect of the vectorized engine on interactive workload of graph queries under a single-node environment. © 2023 IEEE.

关键词： Engines

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Enhanced Dcf Tracker Regularized by Reliable Sample Construction 48

Enhanced Dcf Tracker Regularized by Reliable Sample Construc...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hu, Kun Cao, Mingyu Wang, Mengzhu Lan, Long Yang, Wenjing Tan, Huibin Institute for Quantum Information State Key Laboratory of High Performance Computing China National University of Defense Technology College of Computer Science and Technology Changsha China

ISBN: (纸本)9781728163277

Discriminative correlation filter (DCF) is a highly efficient tracking technique using the circulant shifted samples of search images to update the template, so the reliability of input samples determines template quality. In this paper, we rethink the reliability problem of input samples in advance during template updating and propose an enhanced DCF tracking method regularized by a novel sparse representation based reliable sample construction term, called enhanced sparse correlation filter (ESCF). Specifically, the reconstructed reliable samples are the sparse representation of circulant shifted samples of unfiltered input samples, in which the target will approach the center to preserve target visual cues into the template when using the cosine window. Besides, we jointly perform template learning and reliable sample construction into a unified learning paradigm to benefit from each other, which further can be carried out in the frequency domain without incurring excessive time cost by skillful decomposition. Experiments on several popular visual tracking datasets verify the efficacy of ESCF and show that ESCF performs favorably against several well-established representative counterparts. © 2023 IEEE.

关键词： Correlation filter Reliable sample construction Sparse representation Unfiltered features

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