An Effective Friend Recommendation Method Using Learning to Rank and Social Influence

Social network sites have become an important medium for people to receive information anytime anywhere. Users of social network sites share information by posting updates. The updates shared by friends form social update streams that provide people with up-to-date information. To receive novel information, users of social network sites are encouraged to establish social relations. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. The information overload problem can result in bad user experiences. It may also affect user intentions to join social network sites and thereby possibly reduce the sites’ advertising earnings which are based on the number of users. To resolve this problem, there is an urgent need of effective friend recommendation methods. A user is considered as a valuable friend if people like the updates the user posts. In this paper, we propose a model-based recommendation method which suggests valuable friends to users. Techniques of matrix factorization and learning to rank are designed to model the latent preferences of users and updates. At the same time, social influence is incorporated into the proposed method to enhance the learned preferences. Valuable friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user. Our experiment findings that are based on a huge real-world dataset demonstrate the effectiveness of the social influence and learning to rank on a friend recommendation task. The results show that the proposed method is effective and it outperforms many well-known friend recommendation methods in terms of the coverage rate and ranking performance

Controlled Morphological Structure of Ceria Nanoparticles Prepared by Spray Pyrolysis

AbstractCeria based materials have been widely used as catalyst supporters and electrolytes. Different applications require different morphologies, and the microstructural control during the synthesis is crucial. In the study, ceria particles were prepared from various precursors using a spray pyrolysis (SP). Comparing to the hollow and porous particles, the formation mechanism with solid spherical structure is not clarified readily. The ceria particles were characterized by transmission electron microscopy, thermogravimetry analysis and X-ray photoelectron spectroscopy. This experimental result suggests that the morphology is controlled by the precursors and could be related to their decomposed behavior during the heating process in SP

Local Residents’ Risk Perceptions in Response to Shale Gas Exploitation: Evidence from China

In 2014, China became the world’s third country to accomplish shale gas commercial development, following the United States and Canada. China still however lacks a comprehensive analysis of its public’s concerns about potential environmental risks of shale gas exploration, particularly those of local residents near extraction sites. This paper specifically aims to explore risks perceived as associated with shale gas development in the Changning-Weiyuan area of Sichuan Basin, by conducting a face-to-face household survey with 730 participants interviewed. Some 86% of respondents reported their belief that shale gas exploitation causes more than three types of negative impacts, the most commonly perceived being noise, underground water contamination and geological disruption. Associated variables that were statistically significant predictors of risk perception include demographic characteristics (age, gender, education), environmental awareness level, landslide experience, awareness of past shale gas accidents, information sources, general knowledge about shale gas, and perspectives on whether negative impacts can be observed and controlled, along with trust in the central government and the petroleum company. Our findings implications are discussed, with the goal of informing both central and local authorities’ policy development in protecting local residents from risks of shale gas exploitation and better communicating risks to residents

Shoot multiplication of Paphiopedilum orchid through in vitro cutting methods

Paphiopedilum orchids are generally propagated through the division of axillary buds from mother plant, which limits commercial production due to its unproductive proliferation and time consuming. In this study, the effect of in vitro cutting methods and medium composition on efficient shoot multiplication of Paphiopedilum Hsinying Rubyweb was investigated. Among three different in vitro stem cutting methods used, vertical cutting was able to produce more new shoots than horizontal and cross cutting when cultured on Hyponex based medium. After 12 weeks of culture, plantlets regenerated from vertical cutting were able to produce new healthy and well rooted shoots higher than without cutting on the same medium. Moreover, the newly-formed shoots which were divided into single plantlets and subcultured onto half-strength Murashige and Skoog (MS) medium without growth regulators could remain higher shoot multiplication than in other media. The micropropagation procedure developed in this study provides a simple means to in vitro propagate Paphiopedilum plantlets which are able to produce large numbers of uniform plantlets in a shorter time compared to the conventional propagation method.Key words: Micropropagation, shoot multiplication, cutting, Paphiopedilum

Game Solving with Online Fine-Tuning

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter. AlphaZero-based heuristics can be highly inaccurate when evaluating these out-of-distribution positions, which occur throughout the entire search. To address this issue, this paper investigates applying online fine-tuning while searching and proposes two methods to learn tailor-designed heuristics for game solving. Our experiments show that using online fine-tuning can solve a series of challenging 7x7 Killall-Go problems, using only 23.54% of computation time compared to the baseline without online fine-tuning. Results suggest that the savings scale with problem size. Our method can further be extended to any tree search algorithm for problem solving. Our code is available at https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023