Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure

Competition-Induced Sign Reversal of Casimir-Lifshitz Torque: An Investigation on Topological Node-Line Semimetal

The dispersion of quasiparticles in topological node-line semimetals is significantly different in different directions. In a certain direction, the quasiparticles behave like relativistic particles with constant velocity. In other directions, they act as two-dimensional electron gas. The competition between relativistic and nonrelativistic dispersions can induce a sign reversal of Casimir-Lifshitz torque. Three different approaches can be applied to generate this sign reversal, i.e., tuning the anisotropic parameter or chemical potential in node-line semimetal, changing the distance between this material and substrate birefringence. Detailed calculations are illustrated for the system with topological node-line semimetal Ca$_3$P$_2$ and liquid crystal material 4-cyano-4-n-pentylcyclohexane-phenyl.Comment: 7 pages, 5 figure

Less is More: Real-time Failure Localization in Power Systems

Cascading failures in power systems exhibit non-local propagation patterns which make the analysis and mitigation of failures difficult. In this work, we propose a distributed control framework inspired by the recently proposed concepts of unified controller and network tree-partition that offers strong guarantees in both the mitigation and localization of cascading failures in power systems. In this framework, the transmission network is partitioned into several control areas which are connected in a tree structure, and the unified controller is adopted by generators or controllable loads for fast timescale disturbance response. After an initial failure, the proposed strategy always prevents successive failures from happening, and regulates the system to the desired steady state where the impact of initial failures are localized as much as possible. For extreme failures that cannot be localized, the proposed framework has a configurable design, that progressively involves and coordinates more control areas for failure mitigation and, as a last resort, imposes minimal load shedding. We compare the proposed control framework with Automatic Generation Control (AGC) on the IEEE 118-bus test system. Simulation results show that our novel framework greatly improves the system robustness in terms of the N-1 security standard, and localizes the impact of initial failures in majority of the load profiles that are examined. Moreover, the proposed framework incurs significantly less load loss, if any, compared to AGC, in all of our case studies

Nearly Optimal Stochastic Approximation for Online Principal Subspace Estimation

Processing streaming data as they arrive is often necessary for high dimensional data analysis. In this paper, we analyse the convergence of a subspace online PCA iteration, as a followup of the recent work of Li, Wang, Liu, and Zhang [Math. Program., Ser. B, DOI 10.1007/s10107-017-1182-z] who considered the case for the most significant principal component only, i.e., a single vector. Under the sub-Gaussian assumption, we obtain a finite-sample error bound that closely matches the minimax information lower bound of Vu and Lei [Ann. Statist. 41:6 (2013), 2905-2947].Comment: 37 page

Rapidity bin multiplicity correlations from a multi-phase transport model

The central-arbitrary bin and forward-backward bin multiplicity correlation patterns for Au+Au collisions at $\sqrt{s_{NN}}$= $7.7-62.4$ GeV are investigated within a multi-phase transport (AMPT) model. An interesting observation is that for $\sqrt{s_{NN}} <19.6$ GeV Au+Au collisions, these two correlation patterns both have an increase with the pseudorapidity gap, while for $\sqrt{s_{NN}} >19.6$ GeV Au+Au collisions, they decrease. We mainly discuss the influence of different evolution stages of collision system on the central-arbitrary bin correlations, such as the initial conditions, partonic scatterings, hadronization scheme and hadronic scatterings. Our results show that the central-arbitrary bin multiplicity correlations have different responses to partonic phase and hadronic phase, which can be suggested as a good probe to explore the dynamical evolution mechanism of the hot dense matter in high-energy heavy-ion collisions.Comment: 7pages, 6 figures, accepted for publication in EPJ

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion