Application of Instantons: Quenching of Macroscopic Quantum Coherence and Macroscopic Fermi-Particle Configurations

Starting from the coherent state representation of the evolution operator with the help of the path-integral, we derive a formula for the low-lying levels $E = \epsilon_0 - 2\triangle\epsilon cos (s+\xi)\pi$ of a quantum spin system. The quenching of macroscopic quantum coherence is understood as the vanishing of $cos (s+\xi)\pi$ in disagreement with the suppression of tunneling (i.e. $\triangle\epsilon = 0$) as claimed in the literature. A new configuration called the macroscopic Fermi-particle is suggested by the character of its wave function. The tunneling rate ($(2\triangle\epsilon)/(\pi)$) does not vanish, not for integer spin s nor for a half-integer value of s, and is calculated explicitly (for the position dependent mass) up to the one-loop approximation.Comment: 13 pages, LaTex, no figure

Characterizing time series : when Granger causality triggers complex networks

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH* human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length

Policy agenda for sustainable intermodal transport in China: An application of the multiple streams framework

Intermodal transport is widely believed to be an efficient way of organizing transportation activities because of its significant role in reducing logistics costs and emissions of air pollutants, which copes with the ever-increasing economic and environmental concerns. This paper applies the multiple streams framework (MSF) to analyze three streams (e.g., the problem stream, policy stream, and politics stream) in setting policy agenda for sustainable intermodal transport in China. By restricting the attention to the opening of the policy window and the coupling of the three streams, the motivation, process, and trend of formulating intermodal transport policy are systematically discussed. The findings show that the key to setting the policy agenda for sustainable intermodal transport in China is to strengthen collaboration among multiple interest groups, boost the national mood, and diversify the identity of policy entrepreneurs. This paper not only verifies the applicability of the MSF, but also helps us to better understand how sustainable intermodal transport policy is formulated in China, thus promoting future policy making

FERM: A FEature-space Representation Measure for Improved Model Evaluation

Understanding whether a particular dataset and task are well represented by a deep learning model can be as crucial as the model's prediction accuracy in many applications. Currently, best prediction performance for large, modern datasets is often achieved by complex and difficult to interpret deep learning models. As deep learning model size and complexity increases compared to the size of the training dataset, the capacity of the model to overfit to inappropriate features and perform poorly or unreliably also increases. Unreliability may not be obvious in traditional performance measures during evaluation so it is important to also consider how well the model is representing the current data distribution. There has previously been little work focusing on measuring this space. We introduce several measures that we collectively name FERM: A FEature-space Representation Measure for determining how well the current feature space representation models the current data distribution and task. We compared our new measures and potential candidates from other related research areas. We demonstrated that our new method, along with two others, have excellent potential to be used for measuring how well a trained model is currently representing a dataset and task. These findings have many implications for deep learning research and applications, including, evaluating when the current model is no longer representing new data well to reduce the frequency of computationally expensive retraining of models, assessing for hard to evaluate failure modes such as model biases that result in particular input samples being poorly represented, guidance on the best hyperparameters to use when updating models with limited new data

Effects of dust storms on microwave radiation based on satellite observation and model simulation over the Taklamakan desert

International audienceEffects of dust particles on microwave radiation over the Taklamakan desert are studied with use of measurements from the Advanced Microwave Scanning Radiometer (AMSR-E) on the EOS Aqua satellite and a microwave radiation transfer model. Eight observed cases show that the signal from atmospheric dust can be separated from the surface radiation by the fact that the dust particles produce stronger scattering at high frequencies and depolarize the background desert signature. This result of satellite data is consistent with the model simulation

Spin photocurrent, its spectra dependence, and current-induced spin polarization in an InGaAs/InAlAs two-dimensional electron gas

Converse effect of spin photocurrent and current induced spin polarization are experimentally demonstrated in the same two-dimensional electron gas system with Rashba spin splitting. Their consistency with the strength of the Rashba coupling as measured from beating of the Shubnikov-de Haas oscillations reveals a unified picture for the spin photocurrent, current-induced spin polarization and spin orbit coupling. In addition, the observed spectral inversion of the spin photocurrent indicates the system with dominating structure inversion asymmetry.Comment: 13 pages, 4 figure