Influence of low-level Pr substitution on the superconducting properties of YBa2Cu3O7-delta single crystals

We report on measurements on Y1-xPrxBa2Cu3O7-delta single crystals, with x varying from 0 to 2.4%. The upper and the lower critical fields, Hc2 and Hc1, the Ginzburg-Landau parameter and the critical current density, Jc(B), were determined from magnetization measurements and the effective media approach scaling method. We present the influence of Pr substitution on the pinning force density as well as on the trapped field profiles analyzed by Hall probe scanning.Comment: 4 pages, 5 figures, accepted for publication in J. Phys. Conf. Se

An integrated bayesian approach for effective multi-truth discovery

Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truthfinding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truthfinding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truthfinding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Lina Yao, Xiaofei Xu, Xue L

Approximate truth discovery via problem scale reduction

Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Xue Li, Xiaofei Xu, and Lina Ya

Existence problem of proton semi-bubble structure in the 21+2_1^+ state of 34^{34}Si

The fully self-consistent Hartree-Fock (HF) plus random phase approximation (RPA) based on Skyrme-type interaction is used to study the existence problem of proton semi-bubble structure in the $2_1^+$ state of $^{34}$Si. The experimental excitation energy and the B(E2) strength of the $2_1^+$ state in $^{34}$Si can be reproduced quite well. The tensor effect is also studied. It is shown that the tensor interaction has a notable impact on the excitation energy of the $2_1^+$ state and a small effect on the B(E2) value. Besides, its effect on the density distributions in the ground and $2_1^+$ state of $^{34}$Si is negligible. Our present results with T36 and T44 show that the $2_1^+$ state of $^{34}$Si is mainly caused by proton transiton from $\pi 1d_{5/2}$ orbit to $\pi 2s_{1/2}$ orbit, and the existence of a proton semi-bubble structure in this state is very unlikely.Comment: 6 pages, 3 figures, 3 table

Role of street patterns in zone-based traffic safety analysis

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Truth discovery via exploiting implications from multi-source data

Data veracity is a grand challenge for various tasks on the Web. Since the web data sources are inherently unreliable and may provide con icting information about the same real-world entities, truth discovery is emerging as a counter- measure of resolving the con icts by discovering the truth, which conforms to the reality, from the multi-source data. A major challenge related to truth discovery is that different data items may have varying numbers of true values (or multi-truth), which counters the assumption of existing truth discovery methods that each data item should have exactly one true value. In this paper, we address this challenge by exploiting and leveraging the implications from multi-source data. In particular, we exploit three types of implications, namely the implicit negative claims, the distribution of positive/negative claims, and the co-occurrence of values in sources' claims, to facilitate multi-truth discovery. We propose a probabilistic approach with improvement measures that incorporate the three implications in all stages of truth discovery process. In particular, incorporating the negative claims enables multi-truth discovery, considering the distribution of positive/negative claims relieves truth discovery from the impact of sources' behavioral features in the specific datasets, and considering values' co-occurrence relationship compensates the information lost from evaluating each value in the same claims individually. Experimental results on three real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Empowering truth discovery with multi-truth prediction

Truth discovery is the problem of detecting true values from the con icting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence informa- tion in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis

© 2019 Elsevier B.V. Image super-resolution aims to generate high-resolution image based on the given low-resolution image and to recover the details of the image. The common approaches include reconstruction-based methods and interpolation-based methods. However, these existing methods show difficulty in processing the regions of an image with complicated texture. To tackle such problems, fractal geometry is applied on image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method regards the whole image as a single fractal set. That is, it does not distinguish the complexity difference of texture across all regions of an image regardless of smooth regions or texture rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. In this paper, the proposed method produces rational fractal interpolation model with various setting at different regions to adapt to the local texture complexity. In order to facilitate such mechanism, the proposed method is able to segment the image region according to its complexity which is determined by its local fractal dimension. Thus, the image super-resolution process is cast to an optimization problem where local fractal dimension in each region is further optimized until the optimization convergence is reached. During the optimization (i.e. super-resolution), the overall image complexity (determined by local fractal dimension) is maintained. Compared with state-of-the-art method, the proposed method shows promising performance according to qualitative evaluation and quantitative evaluation