THE CREATION AND SPREAD OF TECHNOLOGY AND TOTAL FACTOR PRODUCTIVITY IN CHINA'S AGRICULTURE

The studys overall goal is to create a framework for assessing the trends of China's national and international investment in agricultural research and to measure its impact on total factor productivity. The main methodological contribution is to provide more convincing measures of crop-specific technologies from China's national research program and of those imported from the international agricultural research system. Our results find that from 1980-95, China's total factor productivity for rice, wheat and maize grew rapidly and new technology accounts for most of the productivity growth.Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,

Privatization, Public R&D Policy, and Private R&D Investment in China's Agriculture

Private R&D is a major source of innovation and productivity growth in agriculture worldwide. This paper examines trends and determinants of agricultural R&D in China. Results show that while the public sector monopolized agricultural research until recently, private agricultural R&D has grown rapidly since 2000, driven largely by agribusiness privatization. Public-sector R&D investments in basic research also encouraged private R&D research, but public investments in technology development crowded out private R&D investment. China’s private R&D investment would grow more rapidly if the government shifted public resources from technology development to basic research.Agriculture, China, Private R&D, Privatization, Public R&D, Research and Development/Tech Change/Emerging Technologies,

Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations

With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance

LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models

Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. However, LLMs trained with SFT sometimes make simple mistakes and result in hallucinations on reasoning tasks such as question-answering. Without external feedback, it is difficult for SFT to learn a good mapping between the question and the desired answer, especially with a small dataset. This paper introduces an alternative to SFT called Natural Language Feedback for Finetuning LLMs (LaFFi). LaFFi has LLMs directly predict the feedback they will receive from an annotator. We find that requiring such reflection can significantly improve the accuracy in in-domain question-answering tasks, providing a promising direction for the application of natural language feedback in the realm of SFT LLMs. Additional ablation studies show that the portion of human-annotated data in the annotated datasets affects the fine-tuning performance.Comment: Paper accepted in Human-Centric Representation Learning workshop at AAAI 2024 (https://hcrl-workshop.github.io/2024/

A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection

This article proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets. The demo code of the proposed method can be linked soon: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.ht

The Production Performance of China's Transforming Agriculture

Productivity Analysis,

Market Emergence and Transition: Arbitrage, Transaction Costs, and Autarky in China's Grain Markets

Using trimonthly Chinese provincial grain prices from 1988 to 1995, we estimate a parity-bounds model of interregional trade for four subperiods to characterize how multiple aspects of market performance change during the process of economic transition. For each period, we estimate the extent to which arbitrage opportunities are realized by traders, the transaction costs between location pairs, and the likelihood that regions do not trade. Trade restrictions cannot explain the pattern of uneven market development over time. Infrastructure bottlenecks, managerial incentive reforms, and production specialization policies, all were likely important factors affecting market performance. Copyright 2002, Oxford University Press.