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Experimental observation of spontaneous symmetry breaking in a quantum phase transition

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 34-39页

作者： Wen Ning Ri-Hua Zheng Jia-Hao Lu Fan Wu Zhen-Biao Yang Shi-Biao Zheng Fujian key laboratory of Quantum information and Quantum Optics College of Physics and Information EngineeringFuzhou UniversityFuzhou 350108China Hefei national laboratory Hefei 230088China

Spontaneous symmetry breaking(SSB)plays a central role in understanding a large variety of phenomena associated with phase transitions,such as superfluid and *** far,the transition from a symmetric vacuum to a macroscopically ordered phase has been substantially *** process bridging these two distinct phases is critical to understanding how a classical world emerges from a quantum phase transition,but so far remains unexplored in *** here report an experimental demonstration of such a process with a quantum Rabi model engineered with a superconducting *** move the system from the normal phase to the superradiant phase featuring two symmetry-breaking field components,one of which is observed to emerge as the classical *** results demonstrate that the environment-induced decoherence plays a critical role in the SSB.

关键词： quantum phase transition Rabi model spontaneous symmetry breaking superconducting circuit Schrodinger cat states

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Constructing the Impossible Differential of Type-II GFN with Boolean Function and Its Application to WARP

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Chinese Journal of Electronics 2024年第1期33卷 80-89页

作者： Jiali SHI Guoqiang LIU Chao LI College of Liberal Arts and Sciences National University of Defense Technology Hunan engineering Research Center of Commercial Cryptography Theory and Technology Innovation State key laboratory of information Security Institute of Information Engineering

Type-Ⅱ generalized Feist el network(GFN) has attracted a lot of attention for its simplicity and high *** differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like *** deduce the impossible differential of Type-Ⅱ GFN by analyzing the Boolean function in the middle *** main idea is to investigate the expression with the variable representing the plaintext(ciphertext) difference words for the internal state *** adopting the miss-in-the-middle approach,we can construct the impossible differential of Type-Ⅱ *** an illustration,we apply this approach to WARP,a lightweight 128-bit block cipher with a 128-bit key which was presented by Banik et *** SAC *** structure of WARP is a 32-branch Type-Ⅱ ***,we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on *** the 32-round key recovery attack on WARP,some observations are used to mount an effective *** the advantage of the early abort technique,the data,time,and memory complexities are 2125.69chosen plaintexts,2126.6832-round encryptions,and 2100-bit,*** the best of our knowledge,this is the best attack on WARP in the single-key scenario.

关键词： Ciphers Boolean functions Parallel processing Complexity theory

Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise

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Chinese Journal of Electronics 2024年第1期33卷 282-292页

作者： Yongpeng CUI Xiaojun SUN School of Electronic engineering Heilongjiang University key laboratory of information Fusion Estimation and Detection

The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter(EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.

关键词： Adaptive systems information filters Matrices Adaptive estimation Kalman filters State estimation Nonlinear systems

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

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Computer Modeling in engineering & Sciences 2024年第9期140卷 2613-2634页

作者： Ziqiao Zhou Tianyang Zhou Jinghao Xu Junhu Zhu Henan key laboratory of information Security National Engineering Technology Research Center of the Digital Switching SystemZhengzhou450000China School of Cryptographic engineering Information Engineering UniversityZhengzhou450000China

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test *** view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target *** RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the ***,most methods still rely on experts’*** address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic ***,the penetration testing problem is formally described in terms of reinforcement *** speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first ***,the method offers an exploration strategy based on episodic memory to guide the agents in *** design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning ***,comparison experiments are carried out with the existing RL-based *** results reveal that the proposed method has better convergence *** running time is reduced by more than 20%.

关键词： Intelligent penetration testing penetration testing path planning reinforcement learning episodic memory exploration strategy

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

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The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian national engineering and Technology Center for information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of ***,it’s essential to construct and estimate a yield-related phenotypic trait considering spike *** on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning *** results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area ***3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike *** spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,*** results provide new insights into the role of fruiting organs for the accurate spectral prediction of *** high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation

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Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

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Computers, Materials & Continua 2024年第4期79卷 1705-1726页

作者： Aodi Liu Na Wang Xuehui Du Dibin Shan Xiangyu Wu Wenjuan Wang He’nan Province key laboratory of information Security Information Engineering UniversityZhengzhou450000China

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business *** BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

关键词： Big data access control data security BiLSTM

Non-adiabatic holonomic quantum operations in continuous variable systems

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Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 12-19页

作者： Hao-Long Zhang Yi-Hao Kang Fan Wu Zhen-Biao Yang Shi-Biao Zheng Fujian key laboratory of Quantum information and Quantum Optics College of Physics and Information EngineeringFuzhou UniversityFuzhou 350116China

Quantum operations by utilizing the underlying geometric phases produced in physical systems are favoured due to their potential *** a system in a non-degenerate eigenstate undergoes an adiabatically cyclic evolution dominated by its Hamiltonian,it will get a geometric phase,referred to as the Berry *** a non-adiabatically cyclic evolution produces an Aharonov-Anandan geometric *** two types of Abelian geometric phases are extended to the non-Abelian cases,where the phase factors become matrix-valued and the transformations associated with different loops are *** and non-Abelian(holonomic)operations are prevalent in discrete variable systems,whose limited(say,two)energy levels,form the *** their developments in continuous systems have also been investigated,mainly due to that,bosonic modes(in,such as,cat states)with large Hilbert spaces,provide potential advantages in fault-tolerant quantum *** we propose a feasible scheme to realize non-adiabatic holonomic quantum logic operations in continuous variable systems with cat *** construct arbitrary single-qubit(two-qubit)gates with the combination of single-and two-photon drivings applied to a Kerr Parametric Oscillator(KPO)(the coupled KPOs).Our scheme relaxes the requirements of the previously proposed quantum geometric operation strategies in continuous variable systems,providing an effective way for quantum control.

关键词： cat qubit continuous variable systems non-Abelian

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Combo Packet:An Encryption Traffic Classification Method Based on Contextual information

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Computers, Materials & Continua 2024年第4期79卷 1223-1243页

作者： Yuancong Chai Yuefei Zhu Wei Lin Ding Li State key laboratory of Mathematical engineering and Advanced Computing Information Engineering UniversityZhengzhou450001China

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.

关键词： Encrypted traffic classification packet-level convolutional neural network attention mechanisms

Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

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Chinese Journal of Electronics 2024年第04期33卷 997-1009页

作者： Yanshan LI Jiarong WANG Kunhua ZHANG Jiawei YI Miaomiao WEI Lirong ZHENG Weixin XIE ATR national key laboratory of Defense Technology Shenzhen University Guangdong key laboratory of Intelligent information Processing Shenzhen University

Existing high-precision object detection algorithms for UAV(unmanned aerial vehicle) aerial images often have a large number of parameters and heavy weight, which makes it difficult to be applied to mobile *** propose three YOLO-based lightweight object detection networks for UAVs, named YOLO-L, YOLO-S, and YOLO-M, respectively. In YOLO-L, we adopt a deconvolution approach to explore suitable upsampling rules during training to improve the detection accuracy. The convolution-batch normalization-Si LU activation function(CBS)structure is replaced with Ghost CBS to reduce the number of parameters and weight, meanwhile Maxpool maximum pooling operation is proposed to replace the CBS structure to avoid generating parameters and weight. YOLO-S greatly reduces the weight of the network by directly introducing CSPGhost Neck residual structures, so that the parameters and weight are respectively decreased by about 15% at the expense of 2.4% mAP. And YOLO-M adopts the CSPGhost Neck residual structure and deconvolution to reduce parameters by 5.6% and weight by 5.7%, while m AP only by 1.8%. The results show that the three lightweight detection networks proposed in this paper have good performance in UAV aerial image object detection task.

关键词： Training YOLO Deconvolution Accuracy Object detection Autonomous aerial vehicles Feature extraction

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