Multi-roles affiliation model for general user profiling

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Forecasting Collector Road Speeds Under High Percentage of Missing Data

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsly cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE

Time-aware Path Reasoning on Knowledge Graph for Recommendation

Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to it's ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as purchase time, recommend time, etc.), which may result in unsuitable explanations. In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. First, we present an efficient time-aware interaction relation extraction component to construct collaborative knowledge graph with time-aware interactions (TCKG for short), and then introduce a novel time-aware path reasoning method for recommendation. We conduct extensive experiments on three real-world datasets. The results demonstrate that the proposed TPRec could successfully employ TCKG to achieve substantial gains and improve the quality of explainable recommendation.Comment: 27 pages, ACM Transactions on Information Systems (TOIS), accepte

Protective Effects of Several Common Amino Acids, Vitamins, Organic Acids, Flavonoids and Phenolic Acids against Hepatocyte Damage Caused by Alcohol

With the increase in alcohol consumption, more and more people are suffering from alcoholic liver disease (ALD). Therefore, it is necessary to elaborate the pathogenesis of ALD from the aspects of alcohol metabolism and harm. In this study, we established an alcoholic liver injury model in vitro by inducing L02 cells with different concentration of ethanol and acetaldehyde. Results showed that the metabolism of ethanol can promote the content of ROS, MDA, TNF-α, IL-6, and caspase 3, causing oxidative and inflammatory stress and membrane permeability changes. However, unmetabolized ethanol and acetaldehyde had little effect on cell membrane permeability and inflammation, indicating that ethanol metabolites were the main reason for cell membrane damage. We also evaluated the effects of amino acids (taurine and methionine), vitamins (E and vitamin D), organic acids (malic acid and citric acid), flavonoids (rutin and quercetin), and phenolic acids (ferulic acid and chlorogenic acid) on alcohol-induced cell membrane damage of L02 cells. Chlorogenic acid, taurine, vitamin E, and citric acid had remarkable effects on improving cell membrane damage. Malic acid, rutin, quercetin, and ferulic acid had obvious therapeutic effects, while vitamin D and methionine had poor therapeutic effects. The relationship between the structure and effect of active ingredients can be further studied to reveal the mechanism of action, and monomers can be combined to explore whether there is a synergistic effect between functional components, in order to provide a certain theoretical basis for the actual study of liver protection

Atomistic insights into sluggish crystal growth in CoNi-containing multi-principal element alloys

Understanding the sluggish kinetics is of great significance for improving the properties of multi-principal element alloys (MPEAs). In this paper, the crystal growth in undercooled CoNi, CoNiFe and CoNiPd alloys was studied by molecular dynamics (MD) to show atomistic insights into sluggish crystal growth kinetics. The added Fe and Pd lead to a decrease in the crystal growth velocity and more significant sluggish crystal growth kinetics was observed in CoNiPd. After minimizing the instantaneous potential energy of atoms adjacent to the Solid/Liquid (S/L) interface, it was found that the decreased crystal growth velocity as the number of principal elements could be accounted by the accompanying change of inherent structure. The exploration of bulk undercooled liquid showed that the diffusion kinetic in liquid does not play a critical role on the sluggish crystal growth kinetics. Besides, the investigation of atomic structure in front of the smooth S/L interface revealed that the sluggish crystal growth kinetics induced by properties of element was associated with the atomic spontaneous ordering degree