Spin correlations in polarizations of P-wave charmonia χcJ\chi_{cJ} and impact on J/ψJ/\psi polarization

Based on a general form of the effective vertex functions for the decays of P-wave charmonia \chicj, angular distribution formulas for the subsequent decays \chicj\rightarrow \jpsi \gamma and \jpsi \to \mu^+\mu^- are derived. The formulas are the same as those obtained in a different approach in the literature. Our formulas are expressed in a more general form, including parity violation effects and the full angular dependence of \jpsi and muon in the cascade decay \chicj\to\jpsi\gamma\to\mu^+\mu^-\gamma. The \chicj polarization observables are expressed in terms of rational functions of the spin density matrix elements of \chicj production. Generalized rotation-invariant relations for arbitrary integer-spin particles are also derived and their expressions in terms of observable angular distribution parameters are given in the $\chi_{c1}$ and $\chi_{c2}$. To complement our previous direct-\jpsi polarization result, we also discuss the impact on the observable prompt-\jpsi polarization. As an illustrative application of our angular distribution formulas, we present the angular distributions in terms of the tree-level spin density matrix elements of $\chi_{c1}$ and $\chi_{c2}$ production in several different frames at the Large Hadron Collider. Moreover, a reweighting method is also proposed to determine the entire set of the production spin density matrix elements of the $\chi_{c2}$, some of which disappear or are suppressed for vanishing higher-order multipole effects making the complete extraction difficult experimentally.Comment: Version published in PRD, 23 pages, 18 figure

K-quantum Nonlinear Jaynes-Cummings Model in Two Trapped Ions

A k-quantum nonlinear Jaynes-Cummings model for two trapped ions interacting with laser beams resonant to k-th red side-band of center-of-mass mode, far from Lamb-Dicke regime, has been obtained. The exact analytic solution showed the existence of quantum collapses and revivals of the occupation of two atoms.Comment: 8 pages, 3 figure

Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at https://github.com/kevin-ssy/Optical-Flow-Guided-Featur

Development of a classification model in disability sport

The principal aim of this study was to develop a classification model in disability sports. Using disability swimming as an example, methods of participant observation, interview, survey and document analysis were undertaken in three empirical studies to develop and clarify the classification model and three elements in swimming classification- (a) the classification process, (b) classifiers and (c) the classification system. First, the swimming classification process was identified as a social process. Members in the classification process socially interacted. The detailed classification process was described, interpreted and discussed. Several features in the classification process were identified. They included interaction among social actors, routinization, rules in the process, resources used by classifiers, power relations among social actors, allocation of rewards and sanctions in the classification process, and conflicts among social actors. Second, the role of classifiers as an agent of social control in disability swimming was examined. Resources used by medical and technical classifiers in the classification process to maintain their role and social order, and the socialization of classifiers in swimming were specifically explored. In addition, the important characteristics of swimming classifiers were identified in the study. Third, classification outcomes in disability swimming were monitored to evaluate the effectiveness of the classification system. Performance and impairment approaches were used in the study. Data of performances and types of impairment of Paralympic swimmers were analysed. The results revealed that the swimming classification system was generally fair but some classes needed to be fine-tuned. In this study elements of the classification model were clarified by integration of the results of the three empirical studies and the classification literature. It is suggested that researchers may use the concepts of the classification model for further investigationin disability sportc lassificationa nd disability sport committees may apply the model to systematicallye valuatet heir own classification systems, processes and classifiers

Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field

Quantum-State Engineering of Multiple Trapped Ions for Center-of-Mass Mode

We propose a scheme to generate a superposition with arbitrary coefficients on a line in phase space for the center-of-mass vibrational mode of N ions by means of isolating all other spectator vibrational modes from the center-of-mass mode. It can be viewed as the generation of previous methods for preparing motional states of one ion. For large number of ions, we need only one cyclic operatin to generate such a superposition of many coherent states.Comment: 14 pages, revte