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

Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption

Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis

RANSAC (random sample consensus) has been widely used as a benchmark algorithm for model fitting in the presence of outliers for more than thirty years. It is robust for outlier removal and rough model fitting, but neither reliable nor efficient enough for many applications where precision and time is critical. Many other algorithms have been proposed for the improvement of RANSAC. However, no much effort has been done to systematically tackle its limitations on model fitting repeatability, quality indication, iteration termination, and multi-model fitting.A new paradigm, named as SASAC (statistical analysis for sample consensus), is introduced in this paper to relinquish the limitations of RANSAC above. Unlike RANSAC that does not consider sampling noise, which is true in most sampling cases, a term named as ? rate is defined in SASAC. It is used both as an indicator for the quality of model fitting and as a criterion for terminating iterative model searching. Iterative least square is advisably integrated in SASAC for optimal model estimation, and a strategy is proposed to handle a multi-model situation.Experiment results for linear and quadratic function model fitting demonstrate that SASAC can significantly improve the quality and reliability of model fitting and largely reduce the number of iterations for model searching. Using the ? rate as an indicator for the quality of model fitting can effectively avoid wrongly estimated model. In addition, SASAC works very well to a multi-model dataset and can provide reliable estimations to all the models. SASAC can be combined with RANSAC and its variants to dramatically improve their performance.</jats:p

Upflows in the upper transition region of the quiet Sun

We investigate the physical meaning of the prominent blue shifts of Ne VIII, which is observed to be associated with quiet-Sun network junctions (boundary intersections), through data analyses combining force-free-field extrapolations with EUV spectroscopic observations. For a middle-latitude region, we reconstruct the magnetic funnel structure in a sub-region showing faint emission in EIT-Fe 195. This funnel appears to consist of several smaller funnels that originate from network lanes, expand with height and finally merge into a single wide open-field region. However, the large blue shifts of Ne VIII are generally not associated with open fields, but seem to be associated with the legs of closed magnetic loops. Moreover, in most cases significant upflows are found in both of the funnel-shaped loop legs. These quasi-steady upflows are regarded as signatures of mass supply to the coronal loops rather than the solar wind. Our observational result also reveals that in many cases the upflows in the upper transition region (TR) and the downflows in the middle TR are not fully cospatial. Based on these new observational results, we suggest different TR structures in coronal holes and in the quiet Sun.Comment: 4 pages, 4 figures, will appear in the Proceedings of the Solar wind 12 conferenc