Understanding alternatives in data analysis activities

Data workers are non-professional data scientists who engage in data analysis activities as part of their daily work. In this position paper, we share past and on-going work to understand data workers’ sense-making practices. We use multidisciplinary approaches to explore their human-tool partnerships. We introduce our current research on the role of alternatives in data analysis activities. Finally, we conclude with open questions and research directions

Capacity sharing strategy with sustainable revenue-sharing contracts

This paper develops a duopoly model to analyse capacity sharing strategy and the optimal revenue-sharing contract under a two-part tariff and examines the effects of capacity sharing, cost, and sharing charges in three scenarios. The paper uses the two-part tariff method and adds a more realistic assumption of incremental marginal costs to improve the research on capacity sharing strategies. The results show that capacity constraints affect the sustainable development of firms. A sustainable revenue-sharing contract can create a win-win situation for both firms and promote capacity sharing. Capacity sharing, cost, and the revenue-sharing rate have different impacts in different scenarios; the optimal revenue-sharing rate and fixed fee can be determined to maximise the profits of firms that share capacity. However, capacity sharing may not improve social welfare. First published online 28 December 202

Partitioning of biomolecules in two-phase aqueous micellar systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1995.Includes bibliographical references (p. 217-226).by Chia-Li Liu.Ph.D

Validation Methodologies for Construction Engineering and Management Research

Validation of results is an important phase in the organization of a researcher’s work. Libraries and the internet offer a number of sources for guidance with respect to conducting validation in a variety of fields. However, construction engineering and management (CEM) is an area for which such information is unavailable. CEM is an interdisciplinary field, comprised of a variety of subjects: human resources management, project planning, social sciences, etc. This broad range means that the choice of appropriate validation methodologies is critical for ensuring a high level of confidence in research outcomes. In other words, the selection of appropriate validation methodologies represents a significant challenge for CEM researchers. To assist civil engineering researchers as well as students undertaking master’s or doctoral CEM studies, this thesis therefore presents a comprehensive review of validation methodologies in this area. The validation methodologies commonly applied include experimental studies, observational studies, empirical studies, case studies, surveys, functional demonstration, and archival data analysis. The author randomly selected 365 papers based on three main perspectives: industry best practices in construction productivity, factors that affect labour productivity, and technologies for improving construction productivity. The validation methodologies that were applied in each category of studies were examined and recorded in analysis tables. Based on the analysis and discussion of the findings, the author summarized the final results, indicating such items as the highest percentage of a particular methodology employed in each category and the top categories in which that methodology was applied. The research also demonstrates a significant increasing trend in the use of functional demonstration over the past 34 years. As well, a comparison of the period from 1980 to 2009 with the period from 2010 to the present revealed a decrease in the number of papers that reported validation methodology that was unclear. These results were validated through analysis of variation (ANOVA) and least significant difference (LSD) analysis. Furthermore, the relationship between the degree of validation and the number of citations is explored. The study showed that the number of citations is positively related to the degree of validations in a specific category, based on the data acquired from the examination of articles in Constructability and Factors categories. However, based on the data acquired from the examination of articles in the year 2010, we failed to conclude that there existed significant difference between clear-validation group and unclear validation group at the 95 % confidence level

Government regulation of emergency supplies under the epidemic crisis

This paper constructs a multi-oligopoly model of emergency supplies and analyses the market equilibrium results under normal conditions and epidemic conditions. The impacts of the degree of change in market demand, externalities, the material cost of emergency supplies and government regulation on the equilibrium results, especially on the prices of emergency supplies, are discussed. The results show that an increase in material cost will lead to low output and social welfare and a high price, under either normal conditions or epidemic conditions. Moreover, under epidemic conditions, the degree of change in market demand, externalities, material cost and the presence and mode of government regulation all have multiple and complex influences on the equilibrium results. Under epidemic conditions, both government output and price regulation can increase the supply of emergency supplies. In addition, when market demand changes drastically, consumer surplus and social welfare can be enhanced by the implementation of regulations. Particularly, price regulation is more effective when there is a high material cost

Privatisation policy with different oligopolistic competition in the public utilities market

This study constructs an oligopoly model in public utilities sector to explore the optimal privatisation policy and the factors affecting equilibrium outcomes and explores the optimal proportion of state-owned shares. We also offer empirical evidence of China’s public utilities from 1985 to 2019 to prove the applicability of model results. The results show that, depending on product differentiation, cost variance, technical level, nationalisation, partial or full privatisation can be optimal. Improving capital efficiency increases social welfare in Model PP, but not in Model PS. Product differentiation improves social welfare at the expense of profits in SS model. In Model PM, technical improvements boost private enterprise profits but induce a decrement in social welfare. A high proportion of state-owned shares fail to improve social welfare in Model SM. In a word, the value range of parameters and competition modes in public utilities sector affect market players’ welfare distribution, which identifies with the empirical analysis of China’s public utilities development

Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency

Image resolution that has close relations with accuracy and computational cost plays a pivotal role in network training. In this paper, we observe that the reduced image retains relatively complete shape semantics but loses extensive texture information. Inspired by the consistency of the shape semantics as well as the fragility of the texture information, we propose a novel training strategy named Temporally Resolution Decrement. Wherein, we randomly reduce the training images to a smaller resolution in the time domain. During the alternate training with the reduced images and the original images, the unstable texture information in the images results in a weaker correlation between the texture-related patterns and the correct label, naturally enforcing the model to rely more on shape properties that are robust and conform to the human decision rule. Surprisingly, our approach greatly improves both the training and inference efficiency of convolutional neural networks. On ImageNet classification, using only 33\% calculation quantity (randomly reducing the training image to 112$\times$112 within 90\% epochs) can still improve ResNet-50 from 76.32\% to 77.71\%. Superimposed with the strong training procedure of ResNet-50 on ImageNet, our method achieves 80.42\% top-1 accuracy with saving 37.5\% calculation overhead. To the best of our knowledge this is the highest ImageNet single-crop accuracy on ResNet-50 under 224$\times$224 without extra data or distillation