On the efficiency of monetary and fiscal policy in open economies

This paper investigates the efficiency of monetary and fiscal policy in a two-country general equilibrium model with monopolistic competition and wage stickiness. When monopoly distortions are completely eliminated, we find that stochastic government spending can affect the efficiency of the global monetary policy that replicates the real allocation under flexible wages. When the stochastic government spending is present, we find that the monopoly distortions can also affect the efficiency of the global monetary policy that replicates the real allocation under flexible wages. The combination of proportional subsidy policies used to completely eliminate monopoly distortions and the monetary policy replicating the real allocation under flexible wages can be improved after we introduce the stochastic government spending. Fiscal policy is found to be unable to replicate the real allocation under flexible wages.New open-economy macroeconomics, Efficiency of global monetary policy, Stochastic government spending, Monopoly distortions

Convertible Bond Underpricing: Renegotiable Covenants, Seasoning and Convergence (Published in "Management Science", Vol. 53, No. 11, November 2007, pp. 1793.1814. )

We investigate the long-standing puzzle on the underpricings of convertible bonds. We hypothesize that the observed underpricing is induced by the possibility that a convertible bond might renegotiate on some of its covenants, e.g., an imbedded put option, in financial difficulties. Consistent with our hypothesis, we find that the initial underpricing is larger for lower rated bonds. The underpricing worsens if the issuer experiences subsequent financial difficulties. However, conditional on no rating downgrades, our main empirical result shows that convertible bond prices do converge to their theoretical prices within two years. This seasoning period is shorter for higher rated convertible bonds.

"Convertible Bond Underpricing: Renegotiable Covenants, Seasoning and Convergence"

We investigate the long-standing puzzle on the underpricings of convertible bonds. We hypothesize that the observed underpricing is induced by the possibility that a convertible bond might renegotiate on some of its covenants, e.g., an imbedded put option, in financial difficulties. Consistent with our hypothesis, we find that the initial underpricing is larger for lower rated bonds. The underpricing worsens if the issuer experiences subsequent financial difficulties. However, conditional on no rating downgrades, our main empirical result shows that convertible bond prices do converge to their theoretical prices within two years. This seasoning period is shorter for higher rated convertible bonds.

Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related

This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in that it allows simultaneous identification of the endmember spectral signatures and the number of endmembers. Previous SD-MMV studies mainly focus on convex relaxations. In this study, we explore the alternative of greedy pursuit, which generally provides efficient and simple algorithms. In particular, we design a greedy SD-MMV algorithm using simultaneous orthogonal matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be closely related to some existing pure pixel search algorithms, especially, the successive projection algorithm (SPA). Thus, a link between SD-MMV and pure pixel search is revealed. We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise---including its identification of the (unknown) number of endmembers---under a sufficiently low noise level. The identification performance of the proposed greedy algorithm is demonstrated through both synthetic and real-data experiments

Information seeking behavior of police officers in Hong Kong: an exploratory study

Open URL - http://www.academic-conferences.org/pdfs/ICICKM10-Booklet.pdfThe information seeking behavior of a random sample of 40 Hong Kong Police Force (HKPF) officers was investigated from the perspectives of: information seeking behavior; type of searching undertaken; level of sophistication of searching; ability to retrieve required information, and use of the HKPF Library (HKPFL). Frameworks such as: the information seeking process (Chowdhury 2004); the information management cycle (Choo, 1998); and the Information seeking of professionals model (Leckie, Pettigrew & Sylvain 1996), were applied. Data gathering methods included: survey; interview; observation; and case study. Results indicate that the respondents are not, overall, effective information seekers. The respondents generally apply simple retrieval techniques despite perceiving them to be less effective than more advanced techniques. The respondents were often unable to effectively frame simple enquiries. A novice member was less effective and slower at retrieving information than an experienced member, suggesting that transfer of organizational members’ knowledge of information seeking to newer members could be valuable. The sampled HKPF members prefer using print materials to electronic materials or web pages, although these formats are also popular. 27 (67.5%) respondents visit the HKPFL two or less times per week, while 36 (90%) respondents visit the HKPFL website two or less times per week. Most respondents use the HKPFL for leisure rather than work related purposes, although this behavior is both position and department sensitive. Most respondents prefer to browse the collections on shelves and seek help from librarians instead of searching the library catalogue. Recommendations for improving HKPF members’ information skills include: information literacy instruction for new recruits; promoting the HKPFL as an information hub; providing guides for use; and further developing the HKPFL to match members’ information needs by improving collections.published_or_final_versionThe 7th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning (ICICKM 2010), Hong Kong, China, 11-12 November 2010. In Proceedings of 7th ICICKM, 2010, p. 11-1

A high performance lossless Bayer image compression scheme

2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Macroblock-based reverse play algorithm for MPEG video streaming

2003-2004 > Academic research: refereed > Refereed conference paperpublished_fina

CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. We will update the camera-ready version and publish the source codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201