Quantum correlations and classical resonances in an open chaotic system

We show that the autocorrelation of quantum spectra of an open chaotic system is well described by the classical Ruelle-Pollicott resonances of the associated chaotic strange repeller. This correspondence is demonstrated utilizing microwave experiments on 2-D n-disk billiard geometries, by determination of the wave-vector autocorrelation C(\kappa) from the experimental quantum spectra S_{21}(k). The correspondence is also established via "numerical experiments" that simulate S_{21}(k) and C(\kappa) using periodic orbit calculations of the quantum and classical resonances. Semiclassical arguments that relate quantum and classical correlation functions in terms of fluctuations of the density of states and correlations of particle density are also examined and support the experimental results. The results establish a correspondence between quantum spectral correlations and classical decay modes in an open systems.Comment: 10 pages, 5 eps figures, "Quantum chaos Y2K" Nobel symposium, to appear in Physica Script

Close-packed dimers on nonorientable surfaces

The problem of enumerating dimers on an M x N net embedded on non-orientable surfaces is considered. We solve both the Moebius strip and Klein bottle problems for all M and N with the aid of imaginary dimer weights. The use of imaginary weights simplifies the analysis, and as a result we obtain new compact solutions in the form of double products. The compact expressions also permit us to establish a general reciprocity theorem.Comment: 13 pages, 1 figure, typo corrected to the version published in Phys. Lett. A 293, 235 (2002

Improving Robot Performance in Partially Observable and Adversarial Environments through Learning

This work aims at improving a robot's performance in partially-observable and adversarial environment by introducing learning algorithms in different layers of a Learning Robot Software Architecture (LRSA). This work can be divided into three major components: Enhanced Perception, Trajectory Prediction using Potential Fields in an Adversarial Environment and LRSA: Learning Robot Software Architecture. In each component, learning algorithms are developed to improve the performance of sub-tasks in the software layer. This work makes significant improvements and introduces novel abilities for robots to achieve better performance in various domains that are partially-observable and adversarial. For evaluation and testing, this research uses various data sets, including open-source data, manually collected data, the RoboCup SPL simulator and Nao Humanoid robots from Softbank. To achieve better performance in a partially-observable and adversarial environment with imperfect information, a robot requires stable and quality perception functionalities, especially in vision system. Therefore, three learning algorithms have been developed to enhance the perception ability in this research. D-Flow is a novel spatial-temporal learning architecture with newly developed parameter-reduced convolutional recurrent cell. This work helps the robot to perform the target area segmentation effectively and efficiently, such as the soccer field or certain terrain. Q-Detector is a Bi-GAN based ROI proposal algorithm which trains an image encoder to propose the region-of-interests containing target object with probability. It significantly improves the performance of robot detection sub-task in the robocup domain especially when the robots are overlapping. ccGMM (Class Conditional Gaussian Mixture Model) is a novel statistical learning algorithm to improve general object detection for binarized images. All of these learning algorithms can work together in an end-to-end perception learning pipeline to greatly improve the perception functionality of the robot. To win a game against the opponent in a partially-observable and adversarial environment, one of the most important tasks is to predict the opponent's moves. In the real world, actions are durative and the robot must be able to reasonably predict a future state. In addition to trajectory prediction, a path planning algorithm is needed to generate a near-optimal plan, based on the predicted trajectory to try to outperform the opponent. In the chapter "Trajectory Prediction with Potential Field Theory In Adversarial Environment", a Deep Learning Encoder-Decoder structured neural network is proposed to learn and predict the opponent's trajectory based on recorded and simulated data. This work also uses the predicted trajectory in a path planner, based on potential fields, in a one-on-one robot soccer game. To test the proposed algorithms and more clearly explain how these learning algorithms could benefit robot's performance. A Learning Robot Software Architecture~(LRSA) is proposed. This layered architecture provides abstract description of robot software functionalities and show where these learning algorithms interact with the core software

North American pinewood nematode disease in China

The pinewood nematode (Bursaphelenchus xylophilus) is an important alien invasive species in China. This pinewood nematode poses a serious threat to Chinese forests. It does have an important impact on forestry, including economic, environmental, ecological and social. Here, I summarize the current situation of the occurrence and development of North American pinewood nematode disease, and combine Chinese practices in pinewood nematode control to propose considerations from five aspects: 1) laws, 2) national department, 3) scientific research, 4) technology promotion and application, and 5) Science popularization and public education, but also in terms of scientific and technological support, technology promotion and application, scientific popularization, and public education to consider the management strategy of pinewood nematode disease in China

Revealing the cosmic web dependent halo bias

Halo bias is the one of the key ingredients of the halo models. It was shown at a given redshift to be only dependent, to the first order, on the halo mass. In this study, four types of cosmic web environments: clusters, filaments, sheets and voids are defined within a state of the art high resolution $N$-body simulation. Within those environments, we use both halo-dark matter cross-correlation and halo-halo auto correlation functions to probe the clustering properties of halos. The nature of the halo bias differs strongly among the four different cosmic web environments we describe. With respect to the overall population, halos in clusters have significantly lower biases in the {$10^{11.0}\sim 10^{13.5}h^{-1}\rm M_\odot$} mass range. In other environments however, halos show extremely enhanced biases up to a factor 10 in voids for halos of mass {$\sim 10^{12.0}h^{-1}\rm M_\odot$}. Such a strong cosmic web environment dependence in the halo bias may play an important role in future cosmological and galaxy formation studies. Within this cosmic web framework, the age dependency of halo bias is found to be only significant in clusters and filaments for relatively small halos \la 10^{12.5}\msunh.Comment: 14 pages, 14 figures, ApJ accepte