Localized transverse bursts in inclined layer convection

We investigate a novel bursting state in inclined layer thermal convection in which convection rolls exhibit intermittent, localized, transverse bursts. With increasing temperature difference, the bursts increase in duration and number while exhibiting a characteristic wavenumber, magnitude, and size. We propose a mechanism which describes the duration of the observed bursting intervals and compare our results to bursting processes in other systems.Comment: 4 pages, 8 figure

Quantum Ignition of Intramolecular Rotation by Means of IR+UV Laser Pulses

Quantum ignition of intramolecular rotation may be achieved as follows: First, a few-cycle infrared (IR) laser pulse excites the torsional vibration in an oriented molecule. Subsequently, a well timed ultrashort ultraviolet (UV) laser pulse induces a Franck-Condon type transition from the electronic ground state to the excited state with approximate conservation of the intramolecular angular momentum. As a consequence, the torsional motion is converted into a unidirectional intramolecular rotation, with high angular momentum (≈ 100 h). The mechanism is demonstrated by means of representative laser driven wave packets which are propagated on ab initio potential energy curves of the model system (4-methyl-cyclohexylidene)fluoromethane

Magnetic Fluctuations in a Charge Ordered State of the One-Dimensional Extended Hubbard Model with a Half-Filled Band

Magnetic properties in a charge ordered state are examined for the extended Hubbard model at half-filling. Magnetic excitations, magnetic susceptibilities and a nuclear spin relaxation rate are calculated with taking account of fluctuations around the mean-field solution. The relevance of the present results to the observation in the 1:1 organic conductors, (TTM-TTP)I$_3$, is discussed.Comment: 4 pages, 3 figures, to be published in J. Phys. Soc. Jpn. Vol.71 (2002) No.

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, especially for autonomous navigation in highly interactive multi-agent environments. To address these issues, we propose three auxiliary tasks with spatio-temporal relational reasoning and integrate them into the standard DRL framework, which improves the decision making performance and provides explainable intermediate indicators. We propose to explicitly infer the internal states (i.e., traits and intentions) of surrounding agents (e.g., human drivers) as well as to predict their future trajectories in the situations with and without the ego agent through counterfactual reasoning. These auxiliary tasks provide additional supervision signals to infer the behavior patterns of other interactive agents. Multiple variants of framework integration strategies are compared. We also employ a spatio-temporal graph neural network to encode relations between dynamic entities, which enhances both internal state inference and decision making of the ego agent. Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents. To validate the proposed method, we design an intersection driving simulator based on the Intelligent Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics and provides explainable intermediate indicators (i.e., internal states, and interactivity scores) for decision making.Comment: 18 pages, 14 figure

Complex Langevin Equation and the Many-Fermion Problem

We study the utility of a complex Langevin (CL) equation as an alternative for the Monte Carlo (MC) procedure in the evaluation of expectation values occurring in fermionic many-body problems. We find that a CL approach is natural in cases where non-positive definite probability measures occur, and remains accurate even when the corresponding MC calculation develops a severe ``sign problem''. While the convergence of CL averages cannot be guaranteed in principle, we show how convergent results can be obtained in three examples ranging from simple one-dimensional integrals over quantum mechanical models to a schematic shell model path integral.Comment: 19 pages, 10 PS figures embedded in tex