The Dynamics of China's Bio-Fuel Industry and Its Policy Options

The economic development of China following the changes in 1978-79 has transformed the country from a poor nation to being the second largest economy in the world. The change came about as China focused on manufacturing industry as a driver of economic growth. This has led to an increasing demand for energy and thus to a greater reliance on fossil fuels. Concerns for greenhouse gas (GHG) emissions and global warming have also created a huge pressure from the world for China to reduce emissions that accompany extensive use of fossil fuels. The sustainability of the growth and the economic stability of the country faces a severe threat as available reserves of crude oil and coal in China and in the world are limited, making it highly unlikely that the country can continue to depend on these resources for its future energy needs. Because China imports more than half of its oil requirement, it needs to find viable alternatives to decrease its trust on continuing import. Biofuels appear as one such alternative and China has invested in setting up manufacturing facilities for producing bio-ethanol from cereals and cassava. However, the existing production has helped substitute only about 8% of oil requirement and 0.45% of its overall energy needs. On the other hand, diversion of grain, sugarcane, soybeans etc for biofuel production creates shortage in the supply of food leading into high prices and need to import food, sugar, and oil that will affect its trade balance negatively. This report investigates the different aspects of the crises of energy and food security that China faces, which will only become more severe in the very near future. The aim of the analysis is to make some recommendations that can help reduce the negative effects of these issues. Analysis shows that China needs to diversify its risks and take major initiatives to increase production of biofuels for this will simultaneously reduce its dependence on oil and reduce GHG emissions. In order to do so, China needs to shift focus from a manufacturing intensive economy toward horizontal and vertical growth of the agriculture sector. While this happens, it will have to use its vast positive balance of payments to import food

Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020

Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information

Focusing on stochastic programming (SP) with covariate information, this paper proposes an empirical risk minimization (ERM) method embedded within a nonconvex piecewise affine decision rule (PADR), which aims to learn the direct mapping from features to optimal decisions. We establish the nonasymptotic consistency result of our PADR-based ERM model for unconstrained problems and asymptotic consistency result for constrained ones. To solve the nonconvex and nondifferentiable ERM problem, we develop an enhanced stochastic majorization-minimization algorithm and establish the asymptotic convergence to (composite strong) directional stationarity along with complexity analysis. We show that the proposed PADR-based ERM method applies to a broad class of nonconvex SP problems with theoretical consistency guarantees and computational tractability. Our numerical study demonstrates the superior performance of PADR-based ERM methods compared to state-of-the-art approaches under various settings, with significantly lower costs, less computation time, and robustness to feature dimensions and nonlinearity of the underlying dependency

Online Leadership for Open Source Project Success: Evidence from the GitHub Blockchain Projects

Blockchain technology has become increasingly popular in recent years. However, only 8% of blockchain open source projects are maintained actively on GitHub. Drawing on the online leadership literature, this study seeks to understand the correlation between leader characteristics and success of blockchain open source projects from the behavioral (knowledge contribution), structural (social capital) and cognitive (openness orientation) dimensions. Considering the unique decentralization nature of blockchain, this study further investigates the contingency effect of blockchain archetypes with empirical evidence from GitHub. Our findings provide novel insights for understanding the determinants of blockchain open source project success and leadership behaviors in the online community

Pre-training Multi-party Dialogue Models with Latent Discourse Inference

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://github.com/EricLee8/MPD_EMVI.Comment: Accepted by ACL 202

A Deep Learning Based Model for Driving Risk Assessment

In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior