Among physical layer encryption schemes,the quantum noise stream cipher(QNSC)has garnered significant attention due to its compatibility with high-speed commercial fiber-optic communication systems.After careful analy...Among physical layer encryption schemes,the quantum noise stream cipher(QNSC)has garnered significant attention due to its compatibility with high-speed commercial fiber-optic communication systems.After careful analysis of the encryption scheme,we reveal that QNSC transmission systems exhibit a security vulnerability in their encoding scheme.This vulnerability limits quantum noise to alerting high-order information bits in plaintext-dependent regions,creating structured ciphertext concealment.Numerical simulation and experimental verification both indicate that an eavesdropper can use quantization-attack to crack this vulnerability.Existing security assessment methods will overestimate the system's security under quantization-attack.In addition,system security demonstrates a strong linear dependence on the plaintext modulation format,rather than the ciphertext modulation format as is widely presumed.To further enhance system security,the probability distribution is further introduced into the encoding process of ciphertext.The experiment results show that we not only achieved random concealment of ciphertext by quantum noise but also enhanced the eavesdropper's symbol error rate by~86%and maximally expanded the key space of the QAM-QNSC system by 2^(27.2).展开更多
Machine learning has advanced the rapid prediction of inorganic materials properties,yet data scarcity for specific properties and capturing thermodynamic stability remains challenging.We propose a framework utilizing...Machine learning has advanced the rapid prediction of inorganic materials properties,yet data scarcity for specific properties and capturing thermodynamic stability remains challenging.We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures,combined with a transfer learning scheme.This approach accurately predicts energy-related properties(e.g.,total energy,energy above the convex hull,energy band gap)and data-scarce mechanical properties(e.g.,bulk and shear modulus).Our model incorporates four-body interactions,capturing periodicity and structural characteristics.It outperforms state-of-the-art models in 8 materials property regression tasks.Also,this model predicts local atomic environments and global structural features better than several models.Transfer learning addresses mechanical property data scarcity,while separate architecture analysis allows application to materials lacking crystal structure information.Our framework’s interpretability aids in understanding elemental contributions,enhancing material design and discovery.Continuous advancements promise further performance improvements,driving efficient and accurate materials property prediction.展开更多
基金National Natural Science Foundation of China(62431024,62575248,U22A2089)Key Technology Research and Development Program of Shandong Province(2023CXPT100)Outstanding Young Scientist Fund of Sichuan Provincial Natural Science Foundation(2025NSFJQ0052)。
文摘Among physical layer encryption schemes,the quantum noise stream cipher(QNSC)has garnered significant attention due to its compatibility with high-speed commercial fiber-optic communication systems.After careful analysis of the encryption scheme,we reveal that QNSC transmission systems exhibit a security vulnerability in their encoding scheme.This vulnerability limits quantum noise to alerting high-order information bits in plaintext-dependent regions,creating structured ciphertext concealment.Numerical simulation and experimental verification both indicate that an eavesdropper can use quantization-attack to crack this vulnerability.Existing security assessment methods will overestimate the system's security under quantization-attack.In addition,system security demonstrates a strong linear dependence on the plaintext modulation format,rather than the ciphertext modulation format as is widely presumed.To further enhance system security,the probability distribution is further introduced into the encoding process of ciphertext.The experiment results show that we not only achieved random concealment of ciphertext by quantum noise but also enhanced the eavesdropper's symbol error rate by~86%and maximally expanded the key space of the QAM-QNSC system by 2^(27.2).
基金used Stampede 2 and Ranch at the Texas Advanced Computing Center and Bridges at the Pittsburg Supercomputing Center through allocation MCB180008 from the Extreme Science and Engineering Discovery Environment(XSEDE)^(64)which was supported by National Science Foundation grant number#1548562,as well as from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)program65+2 种基金which is supported by National Science Foundation grants#2138259,#2138286,#2138307,#2137603,and#2138296A.T.acknowledges the funding support from the National Science Foundation(CMMI 2145759)the National Institutes of Health(1R56AG075690,1R01AG084715,5U01HL146188).
文摘Machine learning has advanced the rapid prediction of inorganic materials properties,yet data scarcity for specific properties and capturing thermodynamic stability remains challenging.We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures,combined with a transfer learning scheme.This approach accurately predicts energy-related properties(e.g.,total energy,energy above the convex hull,energy band gap)and data-scarce mechanical properties(e.g.,bulk and shear modulus).Our model incorporates four-body interactions,capturing periodicity and structural characteristics.It outperforms state-of-the-art models in 8 materials property regression tasks.Also,this model predicts local atomic environments and global structural features better than several models.Transfer learning addresses mechanical property data scarcity,while separate architecture analysis allows application to materials lacking crystal structure information.Our framework’s interpretability aids in understanding elemental contributions,enhancing material design and discovery.Continuous advancements promise further performance improvements,driving efficient and accurate materials property prediction.
