Chang, Ji-Mei

University of Southern California, Department of Curriculum, Teaching, & Special Education, 1989, Ph.D. University of Southern California, School of Education, 1978, M.S. National Chengchi University, Department of Education, 1970, B.A.https://scholarworks.sjsu.edu/erfa_bios/1002/thumbnail.jp

The relationship between tax evasion and tax revenue in Chang, Lai and Chang (1999)

Chang, Lai and Chang (1999) use a micro-founded short-term macroeconomic model, with an imperfectly competitive market, to analyze, among other issues, the relationship between tax evasion and tax revenue. They show that this relationship depends upon the market structure. In particular, when the market becomes perfectly competitive, this relationship can be non monotonic. Although CLC give an intuition of this result, based on the interaction of two opposite effects, they do not make explicit the form of this relationship. The goal of this note is precisely to show that, within the Chang, Lai and Chang (1999) model, one can completely characterize the shape of the relationship between tax evasion and tax revenue under perfect competition. Under some parametric conditions, the tax revenue decreases with tax evasion otherwise, their relationship takes the form of a `Laffer curve'.

Cryptanalysis of Yang-Wang-Chang's Password Authentication Scheme with Smart Cards

In 2005, Yang, Wang, and Chang proposed an improved timestamp-based password authentication scheme in an attempt to overcome the flaws of Yang-Shieh_s legendary timestamp-based remote authentication scheme using smart cards. After analyzing the improved scheme proposed by Yang-Wang-Chang, we have found that their scheme is still insecure and vulnerable to four types of forgery attacks. Hence, in this paper, we prove that, their claim that their scheme is intractable is incorrect. Also, we show that even an attack based on Sun et al._s attack could be launched against their scheme which they claimed to resolve with their proposal.Comment: 3 Page

Publikationsliste PD Dr. Heide Hoffmann - Publikationen zum Ökolandbau

Publikationen von Heide Hoffmann C. Stroemel S. Müller G. Marx N. Künkel Ch.-L. Chang W. Hübner K. Reute

Ressenyes

Index de les obres ressenyades: S. FENSTERMAKER ; C. WEST (eds.), Doing Gender, Doing Difference : inequality, power and institutional chang

Weighted Radon transforms for which the Chang approximate inversion formula is precise

We describe all weighted Radon transforms on the plane for which the Chang approximate inversion formula is precise. Some subsequent results, including the Cormack type inversion for these transforms, are also given

Local Visual Microphones: Improved Sound Extraction from Silent Video

Sound waves cause small vibrations in nearby objects. A few techniques exist in the literature that can extract sound from video. In this paper we study local vibration patterns at different image locations. We show that different locations in the image vibrate differently. We carefully aggregate local vibrations and produce a sound quality that improves state-of-the-art. We show that local vibrations could have a time delay because sound waves take time to travel through the air. We use this phenomenon to estimate sound direction. We also present a novel algorithm that speeds up sound extraction by two to three orders of magnitude and reaches real-time performance in a 20KHz video.Comment: Accepted to BMVC 201

Fitting Precision Electroweak Data with Exotic Heavy Quarks

The 1999 precision electroweak data from LEP and SLC persist in showing some slight discrepancies from the assumed standard model, mostly regarding $b$ and $c$ quarks. We show how their mixing with exotic heavy quarks could result in a more consistent fit of all the data, including two unconventional interpretations of the top quark.Comment: 7 pages, no figure, 2 typos corrected, 1 reference update

OnionNet: Sharing Features in Cascaded Deep Classifiers

The focus of our work is speeding up evaluation of deep neural networks in retrieval scenarios, where conventional architectures may spend too much time on negative examples. We propose to replace a monolithic network with our novel cascade of feature-sharing deep classifiers, called OnionNet, where subsequent stages may add both new layers as well as new feature channels to the previous ones. Importantly, intermediate feature maps are shared among classifiers, preventing them from the necessity of being recomputed. To accomplish this, the model is trained end-to-end in a principled way under a joint loss. We validate our approach in theory and on a synthetic benchmark. As a result demonstrated in three applications (patch matching, object detection, and image retrieval), our cascade can operate significantly faster than both monolithic networks and traditional cascades without sharing at the cost of marginal decrease in precision.Comment: Accepted to BMVC 201