Effective video multicast over wireless internet

With the rapid growth of wireless networks and great success of Internet video, wireless video services are expected to be widely deployed in the near future. As different types of wireless networks are converging into all IP networks, i.e., the Internet, it is important to study video delivery over the wireless Internet. This paper proposes a novel end-system based adaptation protocol calledWireless Hybrid Adaptation Layered Multicast (WHALM) protocol for layered video multicast over wireless Internet. In WHALM the sender dynamically collects bandwidth distribution from the receivers and uses an optimal layer rate allocation mechanism to reduce the mismatches between the coarse-grained layer subscription levels and the heterogeneous and dynamic rate requirements from the receivers, thus maximizing the degree of satisfaction of all the receivers in a multicast session. Based on sampling theory and theory of probability, we reduce the required number of bandwidth feedbacks to a reasonable degree and use a scalable feedback mechanism to control the feedback process practically. WHALM is also tuned to perform well in wireless networks by integrating an end-to-end loss differentiation algorithm (LDA) to differentiate error losses from congestion losses at the receiver side. With a series of simulation experiments over NS platform, WHALM has been proved to be able to greatly improve the degree of satisfaction of all the receivers while avoiding congestion collapse on the wireless Internet

Relation Embedding for Personalised POI Recommendation

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.Comment: 12 pages, 3 figures, Accepted in the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020

Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings

© 1989-2012 IEEE. Learning appropriate metric is critical for effectively capturing complex data characteristics. The metric learning of categorical data with hierarchical coupling relationships and local heterogeneous distributions is very challenging yet rarely explored. This paper proposes a Heterogeneous mEtric Learning with hIerarchical Couplings (HELIC for short) for this type of categorical data. HELIC captures both low-level value-to-attribute and high-level attribute-to-class hierarchical couplings, and reveals the intrinsic heterogeneities embedded in each level of couplings. Theoretical analyses of the effectiveness and generalization error bound verify that HELIC effectively represents the above complexities. Extensive experiments on 30 data sets with diverse characteristics demonstrate that HELIC-enabled classification significantly enhances the accuracy (up to 40.93 percent), compared with five state-of-the-art baselines

Creep and fracture behavior of peak-aged Mg-11Y-5Gd-2Zn-0.5Zr (wt pct)

The tensile-creep and creep-fracture behavior of peak-aged Mg-11Y-5Gd-2Zn-0.5Zr (wt pct) (WGZ1152) was investigated at temperatures between 523 K (250 °C) to 598 K (325 °C) (0.58 to 0.66 T m) and stresses between 30 MPa to 140 MPa. The minimum creep rate of the alloy was almost two orders of magnitude lower than that for WE54-T6 and was similar to that for HZ32-T5. The creep behavior exhibited an extended tertiary creep stage, which was believed to be associated with precipitate coarsening. The creep stress exponent value was 4.5, suggesting that dislocation creep was the rate-controlling mechanism during secondary creep. At T = 573 K (300 °C), basal slip was the dominant deformation mode. The activation energy for creep (Q avg = 221 ± 20 kJ/mol) was higher than that for self-diffusion in magnesium and was believed to be associated with the presence of second-phase particles as well as the activation of nonbasal slip and cross slip. This finding was consistent with the slip-trace analysis and surface deformation observations, which revealed that the nonbasal slip was active. The minimum creep rate and time-to-fracture followed the original and modified Monkman-Grant relationships. The microcracks and cavities nucleated preferentially at grain boundaries and at the interface between the matrix phase and the second phase. In-situ creep experiments highlighted the intergranular cracking evolution

Creep and fracture behavior of peak-aged Mg-11Y-5Gd-2Zn-0.5Zr (wt pct)

The tensile-creep and creep-fracture behavior of peak-aged Mg-11Y-5Gd-2Zn-0.5Zr (wt pct) (WGZ1152) was investigated at temperatures between 523 K (250 °C) to 598 K (325 °C) (0.58 to 0.66 T m) and stresses between 30 MPa to 140 MPa. The minimum creep rate of the alloy was almost two orders of magnitude lower than that for WE54-T6 and was similar to that for HZ32-T5. The creep behavior exhibited an extended tertiary creep stage, which was believed to be associated with precipitate coarsening. The creep stress exponent value was 4.5, suggesting that dislocation creep was the rate-controlling mechanism during secondary creep. At T = 573 K (300 °C), basal slip was the dominant deformation mode. The activation energy for creep (Q avg = 221 ± 20 kJ/mol) was higher than that for self-diffusion in magnesium and was believed to be associated with the presence of second-phase particles as well as the activation of nonbasal slip and cross slip. This finding was consistent with the slip-trace analysis and surface deformation observations, which revealed that the nonbasal slip was active. The minimum creep rate and time-to-fracture followed the original and modified Monkman-Grant relationships. The microcracks and cavities nucleated preferentially at grain boundaries and at the interface between the matrix phase and the second phase. In-situ creep experiments highlighted the intergranular cracking evolution

Willingness to Pay for Better Air Quality: The case of China

Air pollution is a big threat to human beings and has attract worldwide attention from governments and scholars. Based on the survey of happiness in China, this paper attempts to analyze the impact of local air quality on the happiness of individuals, and to evaluate the monetary value of mitigating air pollution. Through merging individual happiness data in a nationally representative survey with daily air quality index (AQI) according to the date and location of each respondent, it calculates the marginal rate of substitution (MRS) between air quality and income, and then estimates respondents’ willingness to pay (WTP) for better air quality. Moreover, it has further explored the differences of WTPs among groups. This study reaches the conclusion that happiness is positively associated with income but negatively correlated with air pollution. Besides, individual happiness is heavily influenced by income, age, gender, health condition, marital status and other variables. Furthermore, WTPs differ greatly among groups and the estimated average WTP of whole sample is 549.36RMB(or 0.90% of annual household income) per year per family for one unit reduction in AQI

AutoDeconJ: a GPU accelerated ImageJ plugin for 3D light field deconvolution with optimal iteration numbers predicting

Light field microscopy is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU accelerated ImageJ plugin for 4.4x faster and accurate deconvolution of light field microscopy data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifact