Scalable Semidefinite Relaxation for Maximum A Posterior Estimation

Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmically, we develop an accelerated variant of the alternating direction method of multipliers (referred to as SDPAD-LR) that can effectively exploit the special structure of the new relaxation. Encouragingly, the proposed procedure allows solving SDR for large-scale problems, e.g., problems on a grid graph comprising hundreds of thousands of variables with multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable of attaining comparable accuracy while exhibiting remarkably improved scalability, in contrast to the commonly held belief that semidefinite relaxation can only been applied on small-scale MRF problems. We have evaluated the performance of SDR on various benchmark datasets including OPENGM2 and PIC in terms of both the quality of the solutions and computation time. Experimental results demonstrate that for a broad class of problems, SDPAD-LR outperforms state-of-the-art algorithms in producing better MAP assignment in an efficient manner.Comment: accepted to International Conference on Machine Learning (ICML 2014

Impact of Labor Protection Laws on the Operating and Financial Risks of Firms: The Case of China

A debate exists regarding the effect of labor protection laws on labor costs. Whether labor protection laws increase or decrease labor costs has implications for risk exposure of affected firms. If the labor costs go up, all else the same, the firm’s breakeven point goes up. Facing increased business risk, the firm must resort to strategies that inhibit the risk exposure, especially if the higher labor costs cannot be transferred, without adverse consequences, to consumers. The strategies include reigning in, if at all possible, operating leverage and financial leverage. Conversely, if the labor costs decrease, a firm’s business risk declines, and the firm has options to increase its operating leverage and/or financial leverage, lower the product price, or do nothing. By examining the Chinese firms’ reactions to the 2007 labor protection laws, we draw conclusions about laws’ directional impact on labor costs. We find that Chinese firms attempt to reduce business risk by lessening labor intensity, and labor-intensive firms are able to reduce the labor intensity at a significantly higher rate than capital-intensive firms. Neither group is able to significantly reduce asset tangibility. We also find that all firms significantly reduce their financial leverages. Consequently, firms’ investments, as measured by sales growth, decline in the post-reform period. These results are consistent with the cost of labor increasing as a result of the stricter labor protection laws

An Empirical Analysis of Software-as-a-Service Development Mode and Its Impacts on Firm Performance

In this paper, we address the following two research questions: (1) Under what circumstances will firms prefer internal SaaS development to external sourcing; and (2) how does the SaaS development mode affect firm performance? We examine the SaaS development actions in the computer industry (SIC code 737) from 2003 to 2012. Preliminary analysis results demonstrate that firms with large amount of working capital can consider developing SaaS application in-house. However, if firms have high level of R&D capability, they may have better absorptive capability of technology innovation. Firms can grasp SaaS innovation through external sourcing. Firms shall also take into account the market characteristics when making the development choice. Our results indicate that the strategic decision of SaaS development mode will have short-term impact on firm performance (i.e., gross margin and market share), but not for the long-run performance (Tobins’q)

Federated Neural Architecture Search

To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it already was. Such difficulty is further amplified when designing and deploying different neural architectures for heterogeneous mobile platforms. In this work, we propose an automatic neural architecture search into the decentralized training, as a new DNN training paradigm called Federated Neural Architecture Search, namely federated NAS. To deal with the primary challenge of limited on-client computational and communication resources, we present FedNAS, a highly optimized framework for efficient federated NAS. FedNAS fully exploits the key opportunity of insufficient model candidate re-training during the architecture search process, and incorporates three key optimizations: parallel candidates training on partial clients, early dropping candidates with inferior performance, and dynamic round numbers. Tested on large-scale datasets and typical CNN architectures, FedNAS achieves comparable model accuracy as state-of-the-art NAS algorithm that trains models with centralized data, and also reduces the client cost by up to two orders of magnitude compared to a straightforward design of federated NAS