Despite the large size of most communication and transportation systems, there are short paths between nodes in these networks which guarantee the efficient information, data and passenger delivery; furthermore these ...Despite the large size of most communication and transportation systems, there are short paths between nodes in these networks which guarantee the efficient information, data and passenger delivery; furthermore these networks have a surprising tolerance under random errors thanks to their inherent scale-free topology. However, their scale-free topology also makes them fragile under intentional attacks, leaving us a challenge on how to improve the network robustness against intentional attacks without losing their strong tolerance under random errors and high message and passenger delivering capacity. Here We propose two methods （SL method and SH method） to enhance scale-free network＇s tolerance under attack in different conditions.展开更多
There is plenty of evidence that proves the beneficial and reliable effects of rehabilitation therapy,making it the most common treatment for patients with chronic stroke.It is believed that rehabilitation improves fu...There is plenty of evidence that proves the beneficial and reliable effects of rehabilitation therapy,making it the most common treatment for patients with chronic stroke.It is believed that rehabilitation improves functional recovery through neural network remodeling,which is observed as a motor map reorganization or functional connectivity change assessed by intracortical microstimulation or functional magnetic resonance imaging（MRI）.展开更多
The reachability of a strongly connected network may be destroyed after link damage.Since many networks are directed or equivalent directed,connected by directed links with the potential for reversal. Therefore the re...The reachability of a strongly connected network may be destroyed after link damage.Since many networks are directed or equivalent directed,connected by directed links with the potential for reversal. Therefore the reachability can be restored by reversing the direction of links.[1]has studied this matter under unlimited resources(transmitter and receiver)condition.In this paper the reconnectability of a net- work with limited number of receivers and transmitters is discussed.Also a linear time algorithm is given to find a reconnected reversal for limited receivers and transmitters.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
Although the no-cloning theorem forbids perfect replication of quantum information, it is sometimes possible to produce large numbers of replicas with vanishingly small error. This phenomenon, known as quantum superre...Although the no-cloning theorem forbids perfect replication of quantum information, it is sometimes possible to produce large numbers of replicas with vanishingly small error. This phenomenon, known as quantum superreplication, can occur for both quantum states and quantum gates. The aim of this paper is to review the central features of quantum superreplication and provide a unified view of existing results. The paper also includes new results. In particular, we show that when quantum superreplication can be achieved, it can be achieved through estimation up to an error of size O（M/N2）, where N and M are the number of input and output copies, respectively. Quantum strategies still offer an advantage for superreplication in that they allow for exponentially faster reduction of the error. Using the relation with estimation, we provide i） an alternative proof of the optimality of Heisenberg scaling in quantum metrology, ii） a strategy for estimating arbitrary unitary gates with a mean square error scaling as log N/N2, and iii） a protocol that generates O（N2） nearly perfect copies of a generic pure state U｜0） while using the corresponding gate U only N times. Finally, we point out that superreplication can be achieved using interactions among k systems, provided that k is large compared to M2/N2.展开更多
基金Project supported in part by the China Scholarships Council (Grant No. 2007103794)the Defence Threat Reduction Agency Award HDTRA1-08-1-0027+5 种基金the James S. McDonnell Foundation 21st Century Initiative in Studying Complex Systems,the National Science Foundation within the DDDAS (CNS-0540348)ITR (DMR-0426737)IIS-0513650 programsthe US Office of Naval Research Award N00014-07-Cthe National Natural Science Foundation of China (Grant Nos. 80678605 and 60903157)the National High Technology Research and Development Program of China (Grant No. 2009AA01Z422)
文摘Despite the large size of most communication and transportation systems, there are short paths between nodes in these networks which guarantee the efficient information, data and passenger delivery; furthermore these networks have a surprising tolerance under random errors thanks to their inherent scale-free topology. However, their scale-free topology also makes them fragile under intentional attacks, leaving us a challenge on how to improve the network robustness against intentional attacks without losing their strong tolerance under random errors and high message and passenger delivering capacity. Here We propose two methods （SL method and SH method） to enhance scale-free network＇s tolerance under attack in different conditions.
基金supported by a Grant-in-Aid for Scientific Research(grant No.17K01493to NO) from the Japan Society for the Promotion of Science
文摘There is plenty of evidence that proves the beneficial and reliable effects of rehabilitation therapy,making it the most common treatment for patients with chronic stroke.It is believed that rehabilitation improves functional recovery through neural network remodeling,which is observed as a motor map reorganization or functional connectivity change assessed by intracortical microstimulation or functional magnetic resonance imaging（MRI）.
文摘The reachability of a strongly connected network may be destroyed after link damage.Since many networks are directed or equivalent directed,connected by directed links with the potential for reversal. Therefore the reachability can be restored by reversing the direction of links.[1]has studied this matter under unlimited resources(transmitter and receiver)condition.In this paper the reconnectability of a net- work with limited number of receivers and transmitters is discussed.Also a linear time algorithm is given to find a reconnected reversal for limited receivers and transmitters.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
文摘Although the no-cloning theorem forbids perfect replication of quantum information, it is sometimes possible to produce large numbers of replicas with vanishingly small error. This phenomenon, known as quantum superreplication, can occur for both quantum states and quantum gates. The aim of this paper is to review the central features of quantum superreplication and provide a unified view of existing results. The paper also includes new results. In particular, we show that when quantum superreplication can be achieved, it can be achieved through estimation up to an error of size O（M/N2）, where N and M are the number of input and output copies, respectively. Quantum strategies still offer an advantage for superreplication in that they allow for exponentially faster reduction of the error. Using the relation with estimation, we provide i） an alternative proof of the optimality of Heisenberg scaling in quantum metrology, ii） a strategy for estimating arbitrary unitary gates with a mean square error scaling as log N/N2, and iii） a protocol that generates O（N2） nearly perfect copies of a generic pure state U｜0） while using the corresponding gate U only N times. Finally, we point out that superreplication can be achieved using interactions among k systems, provided that k is large compared to M2/N2.
