Fighting For Control Power Of GOME Inc.: A Case Study

At 2:30pm, September 28, 2010, a special shareholder meeting for GOME is called by the largest shareholder of GOME, Guangyu Huang, to be held on the first floor of Regal Hotels, 88 Yee Wo Street, Causeway Bay, Hong Kong. GOME Electrical Appliances is one of the largest electrical appliance retailers in Mainland China and Hong Kong. Huang Guangyu, the founder and largest shareholder of GOME, is currently in jail. The purpose of this meeting is to vote on the eight items on the agenda, including deposing the professional manager, Xiao Chen, from the CEO position of GOME. Should the investors support the largest shareholder or the professional manager? It may be the toughest problem GOME has faced in its history. It seems that no matter who wins, the result may not be good news to GOME and its investors. This case is about corporate governance and agency problems and is appropriate for undergraduate and graduate courses in Investment, Corporate Finance and Financial Markets

Differentiation With Shared Features And Cannibalization Of Information Goods

Large sunk cost of development, negligible cost of reproduction and distribution and substantial economies of scale make information goods distinct from industry goods. In this paper, we analyse versioning strategies of horizontally differentiated information goods with shared feature sets, discrete hierarchical groups and continuous individual consumer tastes. Based on our modelling results, when cannibalization is considered among different market segments, it is always sub-optimal to differentiate information goods if market is not fully differentiated or characteristics of the information goods are not specifically designed to relate to certain market segments

How Medical Crowdfunding Helps People? A Large-scale Case Study on Waterdrop Fundraising

While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored. In this paper, we empirically study how online medical crowdfunding helps people using more than 27, 000 fundraising cases in Waterdrop Fundraising, one of the most popular online medical crowdfunding platforms in China. We find that the amount of money obtained by fundraisers is broadly distributed, i.e., a majority of lowly donated cases coexist with a handful of very successful cases. We further investigate the factors that potentially correlate with the success of medical fundraising cases. Profile information of fundraising cases, e.g., geographic information of fundraisers, affects the donated amounts, since detailed description may increase the credibility of a fundraising case. One prominent finding lies in the effect of social network on the success of fundraising cases: the spread of fundraising information along social network is a key factor of fundraising success, and the social capital of fundraisers play an important role in fundraising. Finally, we conduct prediction of donations using machine learning models, verifying the effect of potential factors to the success of medical crowdfunding. Altogether, this work presents a data-driven view of medical fundraising on the web and opens a door to understanding medical crowdfunding.Comment: Accepted as a full paper at ICWSM 202

Inducing Causal Structure for Abstractive Text Summarization

The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach

Slimmable Generative Adversarial Networks

Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models makes them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power. In this paper, we introduce slimmable GANs (SlimGANs), which can flexibly switch the width of the generator to accommodate various quality-efficiency trade-offs at runtime. Specifically, we leverage multiple discriminators that share partial parameters to train the slimmable generator. To facilitate the \textit{consistency} between generators of different widths, we present a stepwise inplace distillation technique that encourages narrow generators to learn from wide ones. As for class-conditional generation, we propose a sliceable conditional batch normalization that incorporates the label information into different widths. Our methods are validated, both quantitatively and qualitatively, by extensive experiments and a detailed ablation study.Comment: Accepted to AAAI 202