Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

GTM-based service map to identify new service opportunities

This study proposes a unique approach for developing and utilising generative topographic mapping (GTM)-based service maps to discover new opportunities of smartphone application service (SAS) with a case of App Store. Since GTM is a probabilistic approach of mapping multidimensional data space onto a low-dimensional latent space and vice versa, it contributes to the automatic detection and interpretation of derived service vacuums that must be intensively investigated to discover new opportunities of SAS. The main contributions and potential utilities of this study are threefold. First, this study theoretically contributes to new service development (NSD) research in mobile service area by proposing an intelligent approach. Secondly, this study is exploratory in that the inverse mapping of a GTM model is first proposed. Lastly, since all activities in the process could be computerised, researchers, engineers, managers, etc., who are interested in NSD can save considerable time and effort when uncovering new service opportunities in terms of managerial implications. Copyright ??? 2015 Inderscience Enterprises Ltdopen

An instrument for discovering new mobile service opportunities

This study proposes a systematic approach to discovering new mobile service opportunities from related documents to overcome the weaknesses of previous methods based on the judgements of experts. At the heart of the suggested approach is text mining to explicitly specify the meanings of documents and information visualisation to effectively explore critical implications. Specifically, we integrate the strengths of Principal Component Analysis (PCA) for mapping multi-dimensional data on a two-dimensional display and the merits of Formal Concept Analysis (FCA) for grouping objects with shared properties based on the lattice theory. A case study of navigation services is presented to show the feasibility of our approach. We believe that the systematic processes and visualised outcomes offered by the proposed approach can enhance the efficiency of mobile service creation and serve as a starting point for developing more general models.open

Rising Inequality in Asia and Policy Implications

WP 2014-14 April 2014JEL Classification Codes: D63; O15; O5

Involvement of Sphingosine 1-Phosphate (SIP)/S1P3 Signaling in Cholestasis-Induced Liver Fibrosis

Bioactive sphingosine 1-phosphate (S1P) and S1P receptors (S1PRs) have been implicated in many critical cellular events, including inflammation, cancer, and angiogenesis. However, the role of S1P/S1PR signaling in the pathogenesis of liver fibrosis has not been well documented. In this study, we found that S1P levels and S1P3 receptor expression in liver tissue were markedly up-regulated in a mouse model of cholestasis-induced liver fibrosis. In addition, the S1P3 receptor was also expressed in green fluorescent protein transgenic bone marrow (BM)-derived cells found in the damaged liver of transplanted chimeric mice that underwent bile duct ligation. Silencing of S1P3 expression significantly inhibited S1P-induced BM cell migration in vitro. Furthermore, a selective S1P3 receptor antagonist, suramin, markedly reduced the number of BM-derived cells during cholestasis. Interestingly, suramin administration clearly ameliorated bile duct ligation-induced hepatic fibrosis, as demonstrated by attenuated deposition of collagen type I and III, reduced smooth muscle α-actin expression, and decreased total hydroxyproline content. In conclusion, our data suggest that S1P/S1P3 signaling plays an important role in cholestasis-induced liver fibrosis through mediating the homing of BM cells. Modulation of S1PR activity may therefore represent a new antifibrotic strategy

Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database

Technology opportunity analysis has been the subject of many prior studies, although most of them have focused on discovering new technology ideas in a single narrow domain. This study proposes a product landscape analysis to identify product areas (i.e., potential technology opportunities) across multiple domains that firms can enter based on the technological capabilities embodied in their existing products. First, text mining is used to construct an integrated patent-product database from the United States patent and trademark database. Second, word2vec is employed to construct a product landscape as a vector space model where products with similar technological bases are located close to each other while maintaining the technological relationships. Third, given a product of interest, potential technology opportunities are identified via (1) automatic opportunity analysis that identifies product areas with technological bases similar to those of the product; and (2) interactive opportunity analysis that finds product areas based on experts' queries modifying the technological bases of the product (i.e., addition and subtraction). Finally, ten quantitative indexes are developed to explore the implications of the potential technology opportunities identified. The case study covering 3,016,315 patents and 160,832 products confirms that the proposed approach is valuable as a creativity support tool for technology opportunity analysis in the era of convergence