<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks."
Abstract:"Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physicalsocial networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users' check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users' check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physicalsocial (CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two largescale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall. (C) 2017 Elsevier B.V. All rights reserved.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks
Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physicalsocial networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users' check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users' check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physicalsocial (CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two largescale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall. (C) 2017 Elsevier B.V. All rights reserved.