Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius

A common difficulty in applications of machine learning is the lack of any general principle for guiding the choices of key parameters of the underlying neural network. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, we uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized. In a three-dimensional representation of the error versus time and spectral radius, the interval corresponds to the bottom region of a "valley." Such a valley arises for a variety of spatiotemporal dynamical systems described by nonlinear partial differential equations, regardless of the structure and the edge-weight distribution of the underlying reservoir network. We also find that, while the particular location and size of the valley would depend on the details of the target system to be predicted, the interval tends to be larger for undirected than for directed networks. The valley phenomenon can be beneficial to the design of optimal reservoir computing, representing a small step forward in understanding these machine-learning systems.Comment: 15 pages, 13 figure

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Noise-enabled species recovery in the aftermath of a tipping point

ACKNOWLEDGMENT We would like to acknowledge support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828.Peer reviewedPublisher PD

Predicting tipping points in mutualistic networks through dimension reduction

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1714958115/-/DCSupplemental.Peer reviewedPublisher PD

An interaction between the SRP receptor and the translocon is critical during cotranslational protein translocation

The signal recognition particle (SRP)-dependent targeting pathway facilitates rapid, efficient delivery of the ribosome-nascent chain complex (RNC) to the protein translocation channel. We test whether the SRP receptor (SR) locates a vacant protein translocation channel by interacting with the yeast Sec61 and Ssh1 translocons. Surprisingly, the slow growth and cotranslational translocation defects caused by deletion of the transmembrane (TM) span of yeast SRbeta (SRbeta-DeltaTM) are exaggerated when the SSH1 gene is disrupted. Disruption of the SBH2 gene, which encodes the beta subunit of the Ssh1p complex, likewise causes a growth defect when combined with SRbeta-DeltaTM. Cotranslational translocation defects in the ssh1DeltaSRbeta-DeltaTM mutant are explained by slow and inefficient in vivo gating of translocons by RNCs. A critical function for translocation channel beta subunits in the SR-channel interaction is supported by the observation that simultaneous deletion of Sbh1p and Sbh2p causes a defect in the cotranslational targeting pathway that is similar to the translocation defect caused by deletion of either subunit of the SR