Improving the accuracy of early diagnosis is the key to prolong the survival of lung cancer. Lung Nodule Detection algorithms based on Deep Learning have made significant contributions to improving the accuracy. Howev...
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ISBN:
(纸本)9783031301100;9783031301117
Improving the accuracy of early diagnosis is the key to prolong the survival of lung cancer. Lung Nodule Detection algorithms based on Deep Learning have made significant contributions to improving the accuracy. However, it remains a challenge to reduce the False Positive rate while maintaining high sensitivity. In this paper, we propose a novel MLP-based False Positive Reduction network, Wave-Involution MLP. We design a progressive multi-scale fusion block based on the novel operator Involution to fuse global features preferably. Moreover, inspired by quantum theory, we design a CT-WaveMLP feature extraction backbone, which transforms CT images into wave functions and enhances feature extraction capability. We performed experiments on LUNAV2 dataset, and the results show that our network achieves the average CPM of 0.861, which has a better performance compared with mainstream methods.
The article is devoted to the development of methods and architecture of optical color computing, techniques of transforming color information for textual representation and numerical calculation, including transmissi...
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Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks ...
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There is a rising need for healthcare as a result of rising public awareness of health issues. Electronic medical records include extremely confidential and sensitive information, and blockchain technology can enable ...
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There is a rising need for healthcare as a result of rising public awareness of health issues. Electronic medical records include extremely confidential and sensitive information, and blockchain technology can enable the safe exchange of these records across various med-ical organizations. The current blockchain system is susceptible to quantum computer attacks, nevertheless, as a result of the advent of quantum computers. This research designs a novel distributed quantum electronic medical record system and suggests a new private quantum blockchain network based on security considerations. The blocks in this quantum blockchain's data structure are linked via entangled states. The time stamp is automatically formed by connecting quantum blocks with controlled activities, which lowers the amount of storage space needed. Each block's hash value is recorded using just one qubit. The quantuminformationprocessing is detailed in depth in the quantum elec-tronic medical record protocol. Every medical record can be tracked, and the security and privacy of electronic medical records in Internet of Medical Things systems can be guaran-teed. The protocol also ditches the traditional encryption and digital signature algorithms in favor of a quantum authentication system. According to the mathematical analysis, the quantum blockchain network has strong security against attacks from quantum computers since it can withstand External attack, Intercept-Measure-Repeat attack and Entanglement-Measure attack. The quantum circuit diagram for deriving the hash value is provided, along with the correctness and traceability analysis of the quantum block. The comparison between the proposed quantum blockchain model and a few other quan-tum blockchain models is also included.(c) 2022 Elsevier Inc. All rights reserved.
In quantum noise stream cipher(QNSC)systems,it is difficult to compensate fiber nonlinearity by digital signal processing(DSP)due to interactions between chromatic dispersion(CD),amplified spontaneous emission(ASE)noi...
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In quantum noise stream cipher(QNSC)systems,it is difficult to compensate fiber nonlinearity by digital signal processing(DSP)due to interactions between chromatic dispersion(CD),amplified spontaneous emission(ASE)noise from erbiumdoped fiber amplifier(EDFA)and Kerr *** equalizer(NLE)based on machine learning(ML)algorithms have been extensively ***,most NLE based on supervised ML algorithms have high training overhead and computation *** addition,the performance of these algorithms have a lot of *** paper proposes two clustering algorithms based on Fuzzylogic C-Means Clustering(FLC)to compensate the fiber nonlinearity in quadrature amplitude modulation(QAM)-based QNSC system,including FLC based on subtractive clustering(SC)and annealing evolution(AE)*** performance of FLC-SC and FLC-AE are evaluated through simulation and *** proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation *** the simulation,different parameter configurations are considered,including fiber length,optical signal-to-noise ratio(OSNR),clipping ratio and resolution of digital to analog converter(DAC).Further-more,we measure the Q-factor of transmission signal with different launched powers,DAC resolution and laser linewidth in the optical back-to-back(BTB)experiment with 80-km single mode *** simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.
Due to the powerful computing capability of quantum computers, cryptographic researchers have applied quantumalgorithms to cryptanalysis and obtained many interesting results. Finding the linear structure of a vector...
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Due to the powerful computing capability of quantum computers, cryptographic researchers have applied quantumalgorithms to cryptanalysis and obtained many interesting results. Finding the linear structure of a vector function is an important task in cryptography. In this work, we study how to reduce the complexity of finding linear structure using the Bernstein-Vazirani (BV) algorithm and give a new quantum linear structure finding algorithm with complexity O(n). Our result realizes a quadratic speedup compared with the previous algorithm (with complexity O(n(2))). This leads to a more efficient analysis of symmetric cryptography, that is, the complexity of some previous BV-based attacks reduces from O(n(2)) to O(n). Besides, we find two new applications for this kind of BV-based attack, i.e., related-key attacks on iterated Even-Mansour ciphers and i-round Feistel ciphers with independent round keys.
作者:
Arsoski, Vladimir V.Univ Belgrade
Sch Elect Engn Dept Microelect & Tech Phys POB 35-54Bulevar kralja Aleksandra 73 Belgrade Serbia
quantum computing has the potential to solve many complex algorithms in the domains of optimization, arithmetics, structural search, financial risk analysis, machine learning, image processing, and others. quantum cir...
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quantum computing has the potential to solve many complex algorithms in the domains of optimization, arithmetics, structural search, financial risk analysis, machine learning, image processing, and others. quantum circuits built to implement these algorithms usually require multi-controlled gates as fundamental building blocks, where the multi-controlled Toffoli stands out as the primary example. For implementation in quantum hardware, these gates should be decomposed into many elementary gates, which results in a large depth of the final quantum circuit. However, even moderately deep quantum circuits have low fidelity due to decoherence effects and, thus, may return an almost perfectly uniform distribution of the output results. This paper proposes a different approach for efficient cost multi-controlled gates implementation using the quantum Fourier transform. We show how the depth of the circuit can be significantly reduced using only a few ancilla qubits, making our approach viable for application to noisy intermediate-scale quantum computers. This quantum arithmetic-based approach can be efficiently used to implement many complex quantum gates.
Gravitational Wave (GW) detection is a pivotal field in astrophysics, gaining prominence for its role in deciphering cosmic phenomena. Traditional machine learning algorithms in GW detection, while effective, grapple ...
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Causal inference revealing causal dependencies between variables from empirical data has found applications in multiple subfields of scientific research. A quantum perspective of correlations holds the promise of over...
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Causal inference revealing causal dependencies between variables from empirical data has found applications in multiple subfields of scientific research. A quantum perspective of correlations holds the promise of overcoming the limitation of Reichenbach's principle and enabling causal inference with only observational data. However, it is still not clear how quantum causal inference can provide operational advantages in general cases. Here, we have devised a photonic setup and experimentally realized an algorithm capable of identifying any two-qubit statistical correlations generated by the two basic causal structures under an observational scenario, thus revealing a universal quantum advantage in causal inference over its classical counterpart. We further demonstrate the explainability and stability of our causal discovery method, which is widely sought in data processingalgorithms. Employing a fully observational approach, our result paves the way for studying quantum causality in general settings.
quantum computing is a new discipline combining quantum mechanics and computer science, which is expected to solve technical problems that are difficult for classical computers to solve efficiently. At present, quantu...
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quantum computing is a new discipline combining quantum mechanics and computer science, which is expected to solve technical problems that are difficult for classical computers to solve efficiently. At present, quantumalgorithms and hardware continue to develop at a high speed, but due to the serious constraints of quantum devices, such as the limited numbers of qubits and circuit depth, the fault-tolerant quantum computing will not be available in the near future. Variational quantumalgorithms(VQAs) using classical optimizers to train parameterized quantum circuits have emerged as the main strategy to address these constraints. However, VQAs still have many challenges, such as trainability, hardware noise, expressibility and entangling capability. The fundamental concepts and applications of VQAs are reviewed. Then, strategies are introduced to overcome the challenges of VQAs and the importance of further researching VQAs is highlighted.
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