Wages, labour market, and living standards in China, 1530-1840

Historical wages continue to provide new insights into the long-term development of the economy. In early modern Europe, the standard wage narrative hypothesises a “little divergence” in which England and the Low Countries outperformed other economies between 1500 and 1750. However, our knowledge of Chinese wage history remains considerably limited when it comes to the “great divergence” debate between China and leading economies in Europe. This article contributes to building a wage series in Lower Yangzi China from the sixteenth to the nineteenth centuries. It shows that despite the continued increase of nominal wages over this period, real day wages witnessed a sharp decline between 1620 and 1640, followed by a substantial improvement after1650, until a quick decline between 1740 and 1760. A wage gap between the Lower Yangzi and London may open up in the early eighteenth century, but this implication still awaits further examination considering the measurement limits in the current approach

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.Comment: Int'l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 202

Applying Big Data Technology to University Libraries: A Perspective Based on Service Context

In the era of big data, the construction of university libraries cannot be separated from the support of big data technology. The collection and analysis of data can improve the management efficiency and service quality of university libraries. On the basis of explaining the characteristics of big data and big data related technologies, our study puts forward the application framework of big data technology in university libraries based on the characteristics of university libraries. Based on the perspective of the context, we explore the effects of the application form of big data technology in university libraries on students \u27perception of the context, and establish a theoretical model of how it will affect students\u27 perceived service quality, perceived value and satisfaction the library. Our research can give suggestions to the service innovation practice of university libraries

Quantification and fiscal governance in China, 1400-1800

How did the state obtain, use, and keep numbers for governing purposes? State governance requires knowledge of those to be governed, and good governance requires the state to build up the capacity to gather and utilise knowledge in the form of numbers. In both the premodern and modern world, numbers and calculative practices serve as an instrument to visualise and capture the world far removed from the centre of administration on paper. They transform physical entities into abstract symbols, and they simplify complex things into readable marks. To explore the roles of numbers and calculative practices in fiscal governance, I turn to early modern China as my case of study, tracing back to the fifteenth century when fiscal institutions began to develop alongside changes in social settings. From the mid-fifteenth century onwards, silver became a more stable numeraire for valuing transactions in the Chinese market. This changing socioeconomic circumstance initiated a century-long process of fiscal monetisation, transforming China’s fiscal system from in-kind-based to money-based. The introduction of silvertaelas a standard numeraire in the state’s statistical and accounting system enabled the central government in China to measure incomes and expenditures in local administration, to intervene in the details of fiscal management in local governments, to build up a local budget system, and to predict and monitor local spending with rigid regulations on the use of tax resources. In the face of warfare and fiscal pressure, local budget figures became the basis for actions, enabling the state to reconfigure fiscal revenues between the central and local authorities. When social order was eventually restored in the late seventeenth century, the Chinese state established a more centralised fiscal system. However, state investments in the local government became too low afterwards, causing fiscal governance in China to repeatedly linger between policy targets and real situations encountered in local administration

Evolutionary-Game-Theory-Based Epidemiological Model for Prediction of Infections with Application to Demand Forecasting in Pharmaceutical Inventory Management Problems

Pharmaceuticals play a critical role in the eradication of infectious diseases. Effective pharmaceutical inventory management is important for controlling epidemics since medical resources such as pharmaceuticals, medical staff, and hospitals are limited. In this study, a novel epidemiological model is proposed to evaluate the resource requirements for pharmaceuticals and is applied to analyze different pharmaceutical inventory management strategies. We formulate the relationship between the number of infected individuals and the risk of infection to account for virus mutation. Evolutionary game theory is integrated into an epidemiological model to represent human behavioral choices. The proposed model can be developed to forecast the demand for pharmaceuticals and analyze how human behavior affects the demand of pharmaceuticals. This study found that making people aware of the risk of disease has a positive impact on both reducing the number of infections and managing the pharmaceutical inventory. The main contribution of this study is to enhance areas of research in pharmaceutical inventory management. This study revealed that the correct recognition of the risk of disease leads to appropriate pharmaceutical management. There are a few studies on the application of infectious disease models to inventory control problems. This study provides clues toward proper pharmaceutical management

Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks

Modeling multimodal human behavior has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is that an effective, learned robot policy used for human-robot collaborative tasks must be able to express a high degree of multimodality, predict actions in a temporally consistent manner, and recognize a wide range of frequencies of human actions in order to seamlessly integrate with a human in the control loop. We present Diffusion Co-policy, a method for planning sequences of actions that synergize well with humans during test time. The co-policy predicts joint human-robot action sequences via a Transformer-based diffusion model, which is trained on a dataset of collaborative human-human demonstrations, and directly executes the robot actions in a receding horizon control framework. We demonstrate in both simulation and real environments that the method outperforms other state-of-art learning methods on the task of human-robot table-carrying with a human in the loop. Moreover, we qualitatively highlight compelling robot behaviors that demonstrate evidence of true human-robot collaboration, including mutual adaptation, shared task understanding, leadership switching, and low levels of wasteful interaction forces arising from dissent.Comment: IEEE Robotics and Automation Letters (RA-L), 2023. 8 pages, 7 figures, 3 tables. Supplementary material at https://sites.google.com/view/diffusion-co-policy-hr

Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective

Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification

Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics. In this work, we aim to extend LT-ReID beyond pedestrian recognition to include a wider range of real-world human activities while still accounting for cloth-changing scenarios over large time gaps. This setting poses additional challenges due to the geometric misalignment and appearance ambiguity caused by the diversity of human pose and clothing. To address these challenges, we propose a new approach 3DInvarReID for (i) disentangling identity from non-identity components (pose, clothing shape, and texture) of 3D clothed humans, and (ii) reconstructing accurate 3D clothed body shapes and learning discriminative features of naked body shapes for person ReID in a joint manner. To better evaluate our study of LT-ReID, we collect a real-world dataset called CCDA, which contains a wide variety of human activities and clothing changes. Experimentally, we show the superior performance of our approach for person ReID.Comment: 10 pages, 7 figures, accepted by ICCV 202