The rapid growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy eff...The rapid growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy efficiency.Despite great progress in analog optical computing,the lack of scalable optical nonlinearities and losses in photonic devices pose considerable challenges for power levels,energy efficiency,and signal latency.Here,we report an end-to-end all-optical nonlinear activator that utilizes the energy conversion of Brillouin scattering to perform efficient nonlinear processing.The activator exhibits an ultra-low activation threshold(24 nW),a wide transmission bandwidth(over 40 GHz),strong robustness,and high energy transfer efficiency.These advantages provide a feasible solution to overcome the existing bottlenecks in ONNs.As a proof-of-concept,a series of tasks is designed to validate the capability of the proposed activator as an activation unit for ONNs.Simulations show that the experiment-based nonlinear model outperforms classical activation functions in classification(97.64%accuracy for MNIST and 87.84%for Fashion-MNIST)and regression(with a symbol error rate as low as 0%)tasks.This work provides valuable insights into the innovative design of all-optical neural networks.展开更多
The recent years have witnessed unprecedented growth in digital infrastructure,driven by rapid advances in cloud computing,the Internet of Things(IoT),smart cities,healthcare informatics,and industrial automation.Whil...The recent years have witnessed unprecedented growth in digital infrastructure,driven by rapid advances in cloud computing,the Internet of Things(IoT),smart cities,healthcare informatics,and industrial automation.While these technologies have improved efficiency and connectivity,they have also created complex vulnerabilities that more sophisticated cyber adversaries can exploit.Cybersecurity is no longer a static domain but a constantly evolving field where threats such as ransomware,advanced persistent threats,and zero-day exploits demand adaptive,intelligent,and proactive responses.Emergent technologies such as artificial intelligence(AI),deep learning,distributed architectures,chaos theory,and post-quantum cryptography are transforming the way we conceptualise and implement information security.These approaches offer not only enhanced accuracy and robustness but also scalability,adaptability,and resilience across diverse,resource-limited environments.Consequently,the goal of this Special Issue on Emerging Technologies in Information Security is to compile innovative contributions that advance the boundaries of theory and practice in this swiftly evolving field.展开更多
基金National Key Research and Development Program of China(2021YFA1401100)Innovation Group Project of Sichuan Province(20CXTD0090)。
文摘The rapid growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy efficiency.Despite great progress in analog optical computing,the lack of scalable optical nonlinearities and losses in photonic devices pose considerable challenges for power levels,energy efficiency,and signal latency.Here,we report an end-to-end all-optical nonlinear activator that utilizes the energy conversion of Brillouin scattering to perform efficient nonlinear processing.The activator exhibits an ultra-low activation threshold(24 nW),a wide transmission bandwidth(over 40 GHz),strong robustness,and high energy transfer efficiency.These advantages provide a feasible solution to overcome the existing bottlenecks in ONNs.As a proof-of-concept,a series of tasks is designed to validate the capability of the proposed activator as an activation unit for ONNs.Simulations show that the experiment-based nonlinear model outperforms classical activation functions in classification(97.64%accuracy for MNIST and 87.84%for Fashion-MNIST)and regression(with a symbol error rate as low as 0%)tasks.This work provides valuable insights into the innovative design of all-optical neural networks.
文摘The recent years have witnessed unprecedented growth in digital infrastructure,driven by rapid advances in cloud computing,the Internet of Things(IoT),smart cities,healthcare informatics,and industrial automation.While these technologies have improved efficiency and connectivity,they have also created complex vulnerabilities that more sophisticated cyber adversaries can exploit.Cybersecurity is no longer a static domain but a constantly evolving field where threats such as ransomware,advanced persistent threats,and zero-day exploits demand adaptive,intelligent,and proactive responses.Emergent technologies such as artificial intelligence(AI),deep learning,distributed architectures,chaos theory,and post-quantum cryptography are transforming the way we conceptualise and implement information security.These approaches offer not only enhanced accuracy and robustness but also scalability,adaptability,and resilience across diverse,resource-limited environments.Consequently,the goal of this Special Issue on Emerging Technologies in Information Security is to compile innovative contributions that advance the boundaries of theory and practice in this swiftly evolving field.
