arXiv subjects

Han Gao

Publications and source records attributed to Han Gao.

111 records · Page 7Linked to original sources

arXiv2019

Revisiting Ryskin's Model of Cosmic Acceleration

Cosmic backreaction as an additional source of the expansion of the universe has been a debate topic since the discovery of cosmic acceleration. The major concern is whether the self interaction of small-scale nonlinear structures would source gravity on very large scales. Gregory Ryskin argued against the additional inclusion of gravitational interaction energy of astronomical objects, whose masses are mostly inferred from gravitational effects and hence should already contain all sources with long-range gravity forces. Ryskin proposed that the backreaction contribution to the energy momentum tensor is instead from the rest of the universe beyond the observable patch. Ryskin's model elegantly solves the fine-tuning problem and is in good agreement with the Hubble diagram of Type Ia supernovae. In this article we revisit Ryskin's model and show that it is {\it inconsistent} with at least one of the following statements: (i) the universe is matter-dominated at low redshift ($z\lesssim 2$); (ii) the universe is radiation-dominated at sufficiently high redshift; (iii) matter density fluctuations are tiny ($\lesssim 10^{-4}$) at the recombination epoch.

astro-ph.CO↗

arXiv2019

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence user preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

cs.IR↗

arXiv2014

Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks

Accurate localization for mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. Also the proportion factor of distance is proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. And the normalized nearness degrees are considered as the weighted standards to calculate coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can timely localize the mobile node. The average localization error of PCFL can decline by about 30.4% than the AFLA method.

cs.NI↗