Large-scale solar cycle features of solar photospheric magnetic field

It is well accepted that the solar cycle originates from a magnetohydrodynamics dynamo deep inside the Sun. Many dynamo models have long been proposed based on a lot of observational constraints. In this paper, using 342 NSO/Kitt Peak solar synoptic charts we study the solar cycle phases in different solar latitudinal zones to set further constraints. Our results can be summarized as follows. (1) The variability of solar polar regions' area has a correlation with total unsigned magnetic flux in advance of 5 years. (2) The high-latitude region mainly appears unipolar in the whole solar cycle and its flux peak time lags sunspot cycle for 3 years. (3) For the activity belt, it is not surprised that its phase be the same as sunspot's. (4) The flux peak time of the low-latitude region shifts forward with an average gradient of 32.2 $day/deg$. These typical characteristics may provide some hints for constructing an actual solar dynamo.Comment: 6 pages, 4 figures; Accepted by AdSR

Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction

Trajectory prediction is widespread in mobile computing, and helps support wireless network operation, location-based services, and applications in pervasive computing. However, most prediction methods are based on very coarse geometric information such as visited base transceiver stations, which cover tens of kilometers. These approaches undermine the prediction accuracy, and thus restrict the variety of application. Recently, due to the advance and dissemination of mobile positioning technology, accurate location tracking has become prevalent. The prediction methods based on precise spatiotemporal information are then possible. Although the prediction accuracy can be raised, a massive amount of data gets involved, which is undoubtedly a huge impact on network bandwidth usage. Therefore, employing fine spatiotemporal information in an accurate prediction must be efficient. However, this problem is not addressed in many prediction methods. Consequently, this paper proposes a novel prediction framework that utilizes massive spatiotemporal samples efficiently. This is achieved by identifying and extracting the information that is beneficial to accurate prediction from the samples. The proposed prediction framework circumvents high bandwidth consumption while maintaining high accuracy and being feasible. The experiments in this study examine the performance of the proposed prediction framework. The results show that it outperforms other popular approaches

Generating energetic electron generation through staged-acceleration in the two-plasmon-decay instability in inertial confinement fusion

A new hot-electron generation mechanism in two-plasmon-decay instabilities is described based on a series of 2D, long-term ( 10 ps) particle-in-cell and fluid simulations under parameters relevant to inertial confinement fusion. The simulations show that significant laser absorption and hot-electron generation occur in the nonlinear stage. The hot electrons are stage accelerated from the low-density region to the high-density region. New modes with small phase velocities develop in the low-density region in the nonlinear stage and form the first stage for electron acceleration. Electron-ion collisions are shown to significantly reduce the efficiency of this acceleration mechanis

Tackling Data Bias in Painting Classification with Style Transfer

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transfer improve classifier training using task specific training datasets or domain adaptation. We propose a system to handle data bias in small paintings datasets like the Kaokore dataset while simultaneously accounting for domain adaptation in fine-tuning a model trained on real world images. Our system consists of two stages which are style transfer and classification. In the style transfer stage, we generate the stylized training samples per class with uniformly sampled content and style images and train the style transformation network per domain. In the classification stage, we can interpret the effectiveness of the style and content layers at the attention layers when training on the original training dataset and the stylized images. We can tradeoff the model performance and convergence by dynamically varying the proportion of augmented samples in the majority and minority classes. We achieve comparable results to the SOTA with fewer training epochs and a classifier with fewer training parameters

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress over the duration of a course. In this paper we present a new approach for predicting a student's performance in a programming course, based upon analyzing directly logged data, describing various aspects of their ordinary programming behavior. An evaluation using data logged from a sample of 45 programming students at our University, showed that our approach was an excellent early predictor of performance, explaining 42.49% of the variance in coursework marks - double the explanatory power when compared to the closest related technique in the literature

Scalable Remote Rendering using Synthesized Image Quality Assessment

Depth-image-based rendering (DIBR) is widely used to support 3D interactive graphics on low-end mobile devices. Although it reduces the rendering cost on a mobile device, it essentially turns such a cost into depth image transmission cost or bandwidth consumption, inducing performance bottleneck to a remote rendering system. To address this problem, we design a scalable remote rendering framework based on synthesized image quality assessment. Specially, we design an efficient synthesized image quality metric based on Just Noticeable Distortion (JND), properly measuring human perceived geometric distortions in synthesized images. Based on this, we predict quality-aware reference viewpoints, with viewpoint intervals optimized by the JND-based metric. An adaptive transmission scheme is also developed to control depth image transmission based on perceived quality and network bandwidth availability. Experiment results show that our approach effectively reduces transmission frequency and network bandwidth consumption with perceived quality on mobile devices maintained. A prototype system is implemented to demonstrate the scalability of our proposed framework to multiple clients

Recent development in multimedia e-learning technologies

Multimedia and networking technologies have significantly impacted on our daily activities, particularly in terms of how we learn. Nowadays, classroom teaching no longer simply relies on chalk and blackboard as the prime medium for course dissemination. E-learning technologies have made it possible to provide a virtual classroom environment on the Web through supporting teacher-student and student-student communications, course material distribution as well as online student assessments. They provide students with more control over their learning schedule and pace. On top of this, multimedia technologies further offer students different forms of media to match their learning styles, leading to enhancements of their learning effectiveness. This extended introduction discusses the latest e-learning specific multimedia technologies, their research challenges and future trends from both pedagogical and technological perspectives. We also summarize the papers included in this special issue

Example-based Image Recoloring in Indoor Environment

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery

Modelling the Linkage Between Landscape Metrics and Water Quality Indices of Hydrological Units in Sihu Basin, Hubei Province, China: An Allometric Model

AbstractStudying quantitative relationships between landscape pattern and water quality is a fundamental step to assess the impacts of non-point source pollution. Many hydrological models with multi-functionality have been developed as useful tools to study several key mechanisms in non-point source pollution. In landscape ecological studies, however, the empirical modelling approaches have been dominated with emphasis on the relationships between the landscape metrics and water quality indices. The main techniques for developing those models of landscape-water quality are statistical regression analysis based on linear models. In this article, Allometric models and the traditional multiple linear regression models for estimating the linkage between landscape metrics and water quality were tested in Sihu Basin, Hubei Province, China. The models at patch class level were established in 24 hydrological units of the basin, which took nine water quality indices (EC, pH, SS, DO, COD, TN, TP, NO3--N, NH4+-N) as the dependent variables and eighteen landscape metrics calculated in FRAGSTATS 3.3 as independent variables. The results suggested that, compared with the traditional multiple linear regression models, Allometric models were more suitable for SS, DO, TP, TN, NH4+-N, in which landscape pattern metrics could explain the 80.5%, 77.7%, 58.2%, 43.9%, 67.6% of total variation, respectively. There had little difference between multiple linear regression models and Allometric models for EC and NO3--N. The coefficients of determination in Allometric models were not as strong as that obtained in the multiple linear regression models for pH and COD. The above results indicated that using Allometric model may potentially provide a new way to study the linkage between landscape metrics and water quality indices, which will help protect our regional water resources