Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

Innovation in China: the promise and the challenge in a transition economy

Regarding innovation as an effective engine for sustainable growth model, Chinese government has launched a series of policies and regulations with the aim of becoming world-leader in innovation by 2050. The Chinese enterprises as potential main innovators are also undergoing a rapid transformation in the process of evolving from backroom producers to the world’s leading force of innovation. However, the unique political and cultural circumstances of China as emerging economy means that the innovation process in China looks very differently than it does in the rest of the world. What will China need in terms of institutional changes? Does geographical location advocated in the field of economic geography really work in China? Are there any other factors besides firm- and region-level ones determining the innovative performance of Chinese enterprises? This thesis consisting of three chapters sheds light on issues relating to innovation and tries to answer questions like these

The sustainable potential of efficient air-transportation industry and green innovation in realising environmental sustainability in G7 countries

Air transportation has a deep impact on environmental degradation due to the higher fossil fuel consumption. On the other hand, this industry also embraces the highest innovation that may alter its environmental consequences. However, there is a dearth of empirical evidence that explores the impact of air transportation and eco-innovation on environmental quality. Therefore, this study is a pioneering attempt to examine the role of air-transportation and eco-innovation in reducing environmental degradation in G7 countries using annual data from 1990 to 2019. In doing so, we employed various advance econometric approaches to handle issues arises from panel data such as Pesaran (2007) and Bai and Carrion-I-Silvestre (2009) used to examine the presence of unit root, cross-sectional dependency checked through Pesaran (2015) test, and for parameters heterogeneity through Pesaran and Yamagata (2008). Moreover, the Westerlund and Edgerton (2008) test and Cross Sectional Augmented ARDL were employed to analyse the long run and short run association among variables. The overall results show that air transportation and eco-innovation play an important role in abating environmental deterioration. Air transportation is negatively correlated with carbon emission and PM2.5 exposure (air quality) due to the improved technical structure of aircraft engines and the use of mixed ration or alternative aviation fuels. These results provide valuable suggestions for all stakeholders

Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection

For a machine learning model deployed in real world scenarios, the ability of detecting out-of-distribution (OOD) samples is indispensable and challenging. Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples. The training-based methods require expensive training cost and rely on OOD samples which are not always available, while most training-free methods can not efficiently utilize the prior information from the training data. In this work, we propose an \textbf{O}ptimal \textbf{P}arameter and \textbf{N}euron \textbf{P}runing (\textbf{OPNP}) approach, which aims to identify and remove those parameters and neurons that lead to over-fitting. The main method is divided into two steps. In the first step, we evaluate the sensitivity of the model parameters and neurons by averaging gradients over all training samples. In the second step, the parameters and neurons with exceptionally large or close to zero sensitivities are removed for prediction. Our proposal is training-free, compatible with other post-hoc methods, and exploring the information from all training data. Extensive experiments are performed on multiple OOD detection tasks and model architectures, showing that our proposed OPNP consistently outperforms the existing methods by a large margin.Comment: Accepted by NeurIPS 2023. 19 page

Mortality among drowning rescuers in China, 2013: a review of 225 rescue incidents from the press

BACKGROUND: Drowning is common worldwide. Rescue efforts attempted by untrained bystanders often lead to the death of the primary drowning victim (PDV), the rescuer or both. Our study aimed to inform prevention by identifying risk factors in rescuer drowning. METHODS: Data on drowning rescue incidents reported online in mainland China, 2013, were reviewed. Information on the drowning incidents, PDVs and rescuers were retrieved for analysis. RESULTS: A total of 225 rescue incidents were identified, of which 14 were victim-rescuer drowning incidents (VRDIs) (6.2 %). A person-to-person rescue by swimming to PDVs was the most commonly used method (58.9 %). Resuscitation was given immediately to 35.5 % of PDVs after rescue. The mortality rate of the rescuers (13.3 %) was similar to that of the PDVs (11.5 %) (χ(2) = 0.5, p =0.49). Being an adult (OR = 0.2, 95 % CI: 0.1–0.5) and other than the first rescuer (OR = 0.4, 95 % CI: 0.2–0.9) decreased the risk of rescuers drowning. CONCLUSIONS: Most of the currently employed life-saving methods are dangerous and even potentially life threatening. The idea of “rescuers’ safety first” should be embraced, especially with teenage and child rescuers, who should never be encouraged to rescue others without first guaranteeing their own safety. Promotion of basic rescue skills should be implemented in the general public

Self-Learning Symmetric Multi-view Probabilistic Clustering

Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing view adaptation. Besides, noise or outliers might significantly degrade the overall clustering performance, which are not handled well by most existing methods. In this paper, we propose a novel unified framework for incomplete and complete MVC named self-learning symmetric multi-view probabilistic clustering (SLS-MPC). SLS-MPC proposes a novel symmetric multi-view probability estimation and equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution. Next, graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, SLS-MPC proposes a probabilistic clustering algorithm to adjust clustering assignments by maximizing the joint probability iteratively without category information. Extensive experiments on multiple benchmarks show that SLS-MPC outperforms previous state-of-the-art methods

LLaFS: When Large Language Models Meet Few-Shot Segmentation

This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased information from the annotated support images, LLaFS leverages the vast prior knowledge gained by LLM as an effective supplement and directly uses the LLM to segment images in a few-shot manner. To enable the text-based LLM to handle image-related tasks, we carefully design an input instruction that allows the LLM to produce segmentation results represented as polygons, and propose a region-attribute table to simulate the human visual mechanism and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization. LLaFS achieves state-of-the-art results on multiple datasets, showing the potential of using LLMs for few-shot computer vision tasks.Comment: Accepted to CVPR202

Asymmetric impacts of technology innovation and environmental quality on tourism development in emerging economies

Tourism development contributes to higher economic output and is highly integrated with environmental quality and associated technologies. Although many studies explore the impact of tourism on carbon emissions; however, little is known regarding the effects of environmental pollution and technology innovation on tourism growth. Therefore, this study examines the impact of technology innovation and environmental pollution on inbound tourism in emerging economies. In doing so, we employ a recently developed panel quantiles regression and found that technology innovation and economic growth stimulate inbound tourism while increasing emissions limit tourist arrivals. These effects are not equally observed across all quantiles. Particularly, the impact of technology innovation is highest at higher quantiles, while the impact of the emissions is highest at lower quantiles. These results suggest that inbound tourism is asymmetrically affected by technology innovation and environmental quality of host destinations. Hence, emerging economies should encourage sustainable tourism by integrating green technologies and minimizing ecological hazards