Second Language Socialization in a Bilingual Chat Room: Global and Local Considerations

This paper considers how global practices of English on the Internet intersect with local practices of English in the territorial or national sphere in constructing the language experiences of immigrant learners. Using a multi-contextual approach to language socialization, this paper examines the social and discursive practices in a Chinese/English bilingual chat room and how this Internet chat room provides an additional context of language socialization for two teenage Chinese immigrants in the US. Analysis of discourse, interview, and observational data reveals that a mixed-code variety of English is adopted and developed among the focal youth and their peers around the globe to construct their relationships as bilingual speakers of English and Cantonese. This language variety served to create a collective ethnic identity for these young people and allowed the girls to assume a new identity in speaking English that doesn't follow the social categories of English-speaking Americans versus Cantonese-speaking Chinese in their local American context. This paper makes the case for studying how people navigate across contexts of socialization in the locality of the nation-state and the virtual environments of the Internet to articulate new ways of using English

An evaluation of the prescriptive utility of psychological bias theory in international relations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Political Science, 2005.Includes bibliographical references (p. 81-87).I evaluate the practical utility of psychological bias theory by examining two historical cases - the US decision to cross the 38th parallel in 1950 and the British policy of appeasement towards Germany in the 1930s - asking in each of these whether the theory could have helped policymakers to make better decisions. Drawing from the lessons of these two cases, I argue that psychological bias theory can help foreign-policymakers to improve their decisionmaking capabilities and hence increase their chances of achieving favorable outcomes in international politics. However, even if the prescriptions of the theory are adopted, there is no guarantee that positive outcomes will obtain in every case because outcomes are affected by at least two other factors that one largely cannot control: the availability of information and the misperceptions suffered by one's opponent. I also discuss other research methods that could be used to investigate the utility of the theory: examining how useful its prescriptions have been; looking at whether people can actually correct their psychological biases; and considering whether policymakers should attempt to rectify their biases.by Wilson Leung.S.M

Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

Neutrino Masses, Lepton Flavor Mixing and Leptogenesis in the Minimal Seesaw Model

We present a review of neutrino phenomenology in the minimal seesaw model (MSM), an economical and intriguing extension of the Standard Model with only two heavy right-handed Majorana neutrinos. Given current neutrino oscillation data, the MSM can predict the neutrino mass spectrum and constrain the effective masses of the tritium beta decay and the neutrinoless double-beta decay. We outline five distinct schemes to parameterize the neutrino Yukawa-coupling matrix of the MSM. The lepton flavor mixing and baryogenesis via leptogenesis are investigated in some detail by taking account of possible texture zeros of the Dirac neutrino mass matrix. We derive an upper bound on the CP-violating asymmetry in the decay of the lighter right-handed Majorana neutrino. The effects of the renormalization-group evolution on the neutrino mixing parameters are analyzed, and the correlation between the CP-violating phenomena at low and high energies is highlighted. We show that the observed matter-antimatter asymmetry of the Universe can naturally be interpreted through the resonant leptogenesis mechanism at the TeV scale. The lepton-flavor-violating rare decays, such as $\mu \to e + \gamma$, are also discussed in the supersymmetric extension of the MSM.Comment: 50 pages, 22 EPS figures, macro file ws-ijmpe.cls included, accepted for publication in Int. J. Mod. Phys.

Treatment of Head and Neck Cancers Using Radiotherapy

Radiotherapy is one of the major treatments for head and neck cancers. This chapter discusses the importance of radiotherapy in treating the common types of head and neck cancers, which can be used as a primary treatment or as a postoperative adjuvant treatment to increase the survival of head and neck cancer patients. Because head and neck cancers are likely to be closely surrounded by radiation-sensitive vital organs, the dosimetric superiority of intensity-modulated radiotherapy (IMRT) to achieve highly conformal dose to the planning target volume (PTV) and avoidance of organs at risk (OARs) helps maintain the cornerstone role of radiotherapy in treating the disease. The rationale of IMRT and the treatment planning technique are introduced. Treatment planning of radiotherapy is one of the key procedures in IMRT. The inverse planning process involves many decision-making steps, including PTV and OAR delineation, beam arrangement settings, objective function setting, etc. These important steps are all illustrated in the chapter, with a specific discussion of planning challenges relevant to head and neck cancers. Finally, the promises for further development of IMRT in terms of OARs dose sparing and PTV dose escalation are briefly discussed and reviewed

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods