A new model for the double well potential

A new model for the double well potential is presented in the paper. In the new potential, the exchanging rate could be easily calculated by the perturbation method in supersymmetric quantum mechanics. It gives good results whether the barrier is high or sallow. The new model have many merits and may be used in the double well problem.Comment: 3pages, 3figure

Solar transition region in the quiet Sun and active regions

The solar transition region (TR), in which above the photosphere the tempera- ture increases rapidly and the density drops dramatically, is believed to play an important role in coronal heating and solar wind acceleration. Long-lasting up-flows are present in the upper TR and interpreted as signatures of mass supply to large coronal loops in the quiet Sun. Coronal bright points (BPs) are local heating phenomena and we found a different Doppler-shift pattern at TR and coronal temperatures in one BP, which might be related to the twisted loop system. The dominant energy loss in the lower TR is the Ly-alpha emission. It has been found that most Ly-alpha radiance profiles are stronger in the blue peak, an asymmetry opposite to higher order Lyman lines. This asymmetry is stronger when the downflow in the middle TR is stronger, indicating that the TR flows play an important role in the line formation process. The peak separation of Ly-alpha is found to be larger in coronal holes than in the quiet Sun, reflecting the different magnetic structures and radiation fields between the two regions. The Lyman line profiles are found to be not reversed in sunspot plume and umbra regions, while they are obviously reversed in the surrounding plage region. At TR temperatures, the densities of the sunspot plume and umbra are a factor of 10 lower than of the plage, indicating that the sunspot plasma emitting at TR temperatures is higher and possibly more extended above sunspots than above the plage region.Comment: This paper has been withdrawn by the author because it's not a referred pape

A Palette of Deepened Emotions: Exploring Emotional Challenge in Virtual Reality Games

Recent work introduced the notion of ‘emotional challenge’promising for understanding more unique and diverse player experiences (PX). Although emotional challenge has immediately attracted HCI researchers’ attention, the concept has not been experimentally explored, especially in virtual reality (VR), one of the latest gaming environments. We conducted two experiments to investigate how emotional challenge affects PX when separately from or jointly with conventional challenge in VR and PC conditions. We found that relatively exclusive emotional challenge induced a wider range of different emotions in both conditions, while the adding of emotional challenge broadened emotional responses only in VR. In both experiments, VR significantly enhanced the measured PX of emotional responses, appreciation, immersion and presence. Our findings indicate that VR may be an ideal medium to present emotional challenge and also extend the understanding of emotional (and conventional) challenge in video games

Radiance and Doppler shift distributions across the network of the quiet Sun

The radiance and Doppler-shift distributions across the solar network provide observational constraints of two-dimensional modeling of transition-region emission and flows in coronal funnels. Two different methods, dispersion plots and average-profile studies, were applied to investigate these distributions. In the dispersion plots, we divided the entire scanned region into a bright and a dark part according to an image of Fe xii; we plotted intensities and Doppler shifts in each bin as determined according to a filtered intensity of Si ii. We also studied the difference in height variations of the magnetic field as extrapolated from the MDI magnetogram, in and outside network. For the average-profile study, we selected 74 individual cases and derived the average profiles of intensities and Doppler shifts across the network. The dispersion plots reveal that the intensities of Si ii and C iv increase from network boundary to network center in both parts. However, the intensity of Ne viii shows different trends, namely increasing in the bright part and decreasing in the dark part. In both parts, the Doppler shift of C iv increases steadily from internetwork to network center. The average-profile study reveals that the intensities of the three lines all decline from the network center to internetwork region. The binned intensities of Si ii and Ne viii have a good correlation. We also find that the large blue shift of Ne viii does not coincide with large red shift of C iv. Our results suggest that the network structure is still prominent at the layer where Ne viii is formed in the quiet Sun, and that the magnetic structures expand more strongly in the dark part than in the bright part of this quiet Sun region.Comment: 10 pages,9 figure

Topology of Entanglement in Multipartite States with Translational Invariance

The topology of entanglement in multipartite states with translational invariance is discussed in this article. Two global features are foundby which one can distinguish distinct states. These are the cyclic unit and the quantised geometric phase. Furthermore the topology is indicated by the fractional spin. Finally a scheme is presented for preparation of these types of states in spin chain systems, in which the degeneracy of the energy levels characterises the robustness of the states with translational invariance.Comment: major revision. accepted by EPJ

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM