<|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:"Preference-Based Assistance Map Learning With Robust Adaptive Oscillators."
Abstract:"Recently, lower-limb exoskeletons have demonstrated the ability to enhance human mobility by reducing biological efforts with human-in-the-loop (HIL) optimization. However, this technology is confined to the laboratory, and it is difficult to generalize to daily applications where gaits are more complex and professional equipment is not accessible. To solve this issue, firstly, we present a robust adaptive oscillator (RAO) to synchronize the human-robot movement and extract gait features. Then, we use the Gaussian process regression (GPR) to map the subjects' preferred assistance parameters to gait features. Experiments show that the RAO has a faster convergence rate compared with the traditional adaptive oscillators. Meanwhile, the learning efficiency of the proposed method shows superiority compared with the HIL optimization. The effectiveness of the proposed method is validated by a hip exoskeleton at a speed of 5 km/h with 7 participants. Three muscles which include rectus femoris, tibialis anterior, and medial gastrocnemius are investigated in three conditions: user-preferred assistance (ASS), zero torque (ZT), and normal walking (NW). The results show that all muscles achieve an activity reduction in ASS mode compared with ZT or NW. Meanwhile, there is a statistically significant difference on medial gastrocnemius in ASS mode with respect to both ZT and NW (-15.63 +/- 6.51 % and -8.73 +/- 6.40%, respectively).."
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:

Preference-Based Assistance Map Learning With Robust Adaptive Oscillators
Recently, lower-limb exoskeletons have demonstrated the ability to enhance human mobility by reducing biological efforts with human-in-the-loop (HIL) optimization. However, this technology is confined to the laboratory, and it is difficult to generalize to daily applications where gaits are more complex and professional equipment is not accessible. To solve this issue, firstly, we present a robust adaptive oscillator (RAO) to synchronize the human-robot movement and extract gait features. Then, we use the Gaussian process regression (GPR) to map the subjects' preferred assistance parameters to gait features. Experiments show that the RAO has a faster convergence rate compared with the traditional adaptive oscillators. Meanwhile, the learning efficiency of the proposed method shows superiority compared with the HIL optimization. The effectiveness of the proposed method is validated by a hip exoskeleton at a speed of 5 km/h with 7 participants. Three muscles which include rectus femoris, tibialis anterior, and medial gastrocnemius are investigated in three conditions: user-preferred assistance (ASS), zero torque (ZT), and normal walking (NW). The results show that all muscles achieve an activity reduction in ASS mode compared with ZT or NW. Meanwhile, there is a statistically significant difference on medial gastrocnemius in ASS mode with respect to both ZT and NW (-15.63 +/- 6.51 % and -8.73 +/- 6.40%, respectively).