Analyzing opinion conflicts in an online group discussion: From the perspective of majority and minority influence

Online community and groups often experience heated discussion. This paper examines a WeChat group discussion from the perspective of majority and minority influence to explore the evolvement of the discussion and the be-haviors of group members. Content analysis of 515 messages suggests that opin- ion conflicts between majority and minority evoke discussion engagement and knowledge exchange. There are different patterns of knowledge construction expressions between majority and minority groups. The majority prefer egocentric expression, while the minority prefer allocentric expression. Majority opinion holders have different conflict handling styles compared to minority opinion holders, who are more likely to avoid. Minority group is under great pressure in social interaction, they are easier to receive unfair comments and personal attacks

C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variables, and changes in one factor will not affect the values of the others. Compared to statistical independence, causal disentanglement allows more controllable data generation, improved robustness, and better generalization. However, most existing work assumes unconfoundedness in the discovery process, that there are no common causes to the generative factors and thus obtain only statistical independence. In this paper, we recognize the importance of modeling confounders in discovering causal generative factors. Unfortunately, such factors are not identifiable without proper inductive bias. We fill the gap by introducing a framework entitled Confounded-Disentanglement (C-Disentanglement), the first framework that explicitly introduces the inductive bias of confounder via labels from domain expertise. In addition, we accordingly propose an approach to sufficiently identify the causally disentangled factors under any inductive bias of the confounder. We conduct extensive experiments on both synthetic and real-world datasets. Our method demonstrates competitive results compared to various SOTA baselines in obtaining causally disentangled features and downstream tasks under domain shifts.Comment: accepted to Neurips 202

You are what emojis say about your pictures: Language - independent gender inference attack on Facebook

International audienceThe picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts generated by Facebook to describe the content of pictures, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we study the correlation of picture owner gender with alt-text, and Emojis/Emoticons used by commenters when reacting to these pictures. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. We show that such a privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Activity patterns of collaborative sensemaking in small discussion groups

Sensemaking could be carried out individually or collaboratively. Collaborative sensemaking is a process where a group of people seek or create a shared representation collaboratively to accomplish a shared task. We conduct content analysis on 12 sections (611 minutes of audio recordings) from 6 small discussion groups relating to 2 topics in information organization. We identified 13 types of collaborative sensemaking activities and categorized them into content-related and process-related activities. We also describe 2 typical sensemaking sequences in these discussion groups. The results may advance our understanding about the activities involved in small group sensemaking. The results may also benefit the development of systems and tools to facilitate collaborative sensemaking process

How can computable general equilibrium models serve low-carbon policy? A systematic review

Computable general equilibrium (CGE) models have been widely employed in economic, social, and environmental impact assessments for low-carbon policies. As net-zero transition has become a global trend, it is crucial to conduct a systematic review of how CGE models serve low-carbon policies. This review examined 1002 studies published between 2001 and 2021, revealing the application of CGE models in low-carbon policy analysis by researchers in 51 countries, including the United States, China, Western Europe, Japan, and Australia, while studies in Latin America, Central Asia and Eastern Europe are limited. Climate targets, carbon pricing, and energy policy are the main policy focuses of CGE analysis, while land policy, demand-side actions, and several other policies are less studied. In addition, CGE analysis generally focuses on the economic impacts of carbon reduction, with a growing amount of attention on social and environmental consequences. The structure, database, and model coupling techniques of CGE models are becoming more complex in order to serve the increasingly multifaceted and diverse policy study purposes. This study provides a comprehensive overview of the research trends of CGE studies on low-carbon policies, thus improving our understanding on low-carbon policies, multiple impacts, and CGE models. We also identify the gaps and potential cutting-edge techniques of current low-carbon policy research. Future work should improve the capacity of CGE models to simulate actual ‘second-best’ policies, provide feedback of complex impacts on policy optimization, and enhance dynamic mechanisms on structural change

A Novel Decomposition Method for Manufacture Variations and the Sensitivity Analysis on Compressor Blades

A high accuracy blade manufacture variation decomposition method was proposed to decompose the manufacture variations of compressor blades to systematic variation and non-systematic variation, which could help to clearly quantify the statistical characteristics of the effect of manufacture variations on the blade aerodynamic performance and to guide the modeling of manufacture variations in geometric uncertainty quantification and robust design studies. By conducting the decomposition of manufacture variations with 100 newly manufactured blades of a high-pressure compressor, it was found that the systematic variation could be modeled by using seven representative blade geometry design parameters well and the mean value of the non-systematic variation, which is determined by using the difference between the measured blade and systematically reconstructed blade, is close to zero. For the standard deviation of decomposed manufacture variations, the non-systematic variation accounts for about 40% of the whole, indicating that the systematic variation is the major component of the manufacture variation. However, based on statistical analysis and sensitivity analysis of the effects of the two types of manufacture variations on blade aerodynamic performance, it was found that the mean deviation of the blade loss mainly derives from systematic variations, and the loss dispersion caused by non-systematic variations is significantly greater than that caused by systematic variations. Furthermore, the blade loss at the high incidence angle is most sensitive to the inlet metal angle which belongs to the systematic variation. Meanwhile, the non-systematic variation near the leading-edge is the most sensitive, and it contributes to most of the performance disperse but only accounts for a geometric variation of about 0.45%

To learn or not to learn? The primary school students’ internet use for learning

This study explored primary school students’ usage of Internet for learning, and their attitudes towards using Internet. In this study, we recruited twenty-eight primary school students to write diaries on their Internet usage for learning and conducted focus groups to understand their thoughts and attitudes. The results showed that most primary students in our study regarded the Internet as a tool for fun rather than learning, so they rarely use Internet for learning. This study suggests that social factors like curricular requirements and parents’ attitude towards Internet have much influence on primary school students’ behaviors and attitudes towards the Internet, and provides advice for future guideline to motivate children to make better use of the Internet for their activities including learning in their everyday life

Multi-criteria assessment and optimization of a natural gas-fed solid oxide fuel cell combined heat and power system

Solid oxide fuel cell (SOFC) combined heat and power (CHP) system is a promising candidate for efficient power generation. Due to its complex dynamic characteristics, system performance is still unsatisfied when it operates in variable load conditions. In this study, five improved layouts of a natural gas-fed SOFC-CHP system are proposed using three-fluid heat exchangers innovatively. Performance of the system under different layouts is compared from both energy and exergy perspective and their feasibility in multi-load conditions is evaluated. Sensitivity analysis of four key parameters is conducted to clarify their influences and potential ways for efficiency improvements from both system and component level are explored under different load conditions. Results show that the system with direct combustion of anode off-gas has better adaptability to multi-load conditions. Exergy loss of the combustor always occupies the highest proposition among all the components, at least 30%. Because of the large thermal load and high outlet temperature of the cold fluid, thermal management of the reformer is crucial for the system operation. Changes of the reforming temperature and fuel utilization ratio significantly affect the system performance. As the load increasing, the electric efficiency decreases and the thermal efficiency increases, the total efficiency can always be more than 85% for the SOFC-CHP system