No description available.

No description available.

No description available.

This study explored the perceptions of 74 activity directors responsible for the intergenerational programming that is currently taking place at shared-sites, facilities where older adults and young people receive services and programs simultaneously in a co-located space. Data for this study was collected through a national survey of 149 shared-sites collected from the Generations United data base. the questionnaire asked respondents about their facility’s intergenerational programming, demographic information, and perceived sense of community exhibited by participants in the intergenerational program. Descriptive data regarding the location, primary emphasis, ages and number served, and specific program characteristics, including visual art programming, at IGSS facilities were collected and analyzed. Results from the analysis were reported with limitations. There was a statistical significance suggested in the association of the frequency and duration of art activities with some of the sense of community variables. the study is valuable in determining the current demographics of IGSS facilities that offer visual art programs. Further research needs to be conducted to answer questions regarding the specific role that the visual arts play in creating a sense of community among intergenerational participants at shared-site facilities.

Tsui Ping Tim = 建築物埸景的三維重建 / 徐秉添. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 82-85). / Text in English; abstracts in English and Chinese. / Tsui Ping Tim = Jian zhu wu yi jing de san wei zhong jian / Xu Bingtian. / Acknowledgement --- p.ii / Abstract --- p.iii / Table of Content --- p.v / Chapter Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- A Brief Review on 3D Site Reconstruction --- p.1 / Chapter 1.2. --- Approach of the Project --- p.3 / Chapter 1.3. --- Organization of the Thesis --- p.4 / Chapter 1.3.1 --- The 3D Site Reconstruction --- p.4 / Chapter 1.3.2 --- The Conformal Point Theory --- p.5 / Chapter 1.4. --- Notations --- p.6 / Chapter Chapter 2. --- General System Overview --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Ground Reconstruction --- p.8 / Chapter 2.2.1 --- Planar Homography --- p.9 / Chapter 2.2.2 --- Determination of the Planar Homography --- p.10 / Chapter 2.3 --- Buildings and Cliff Reconstruction --- p.13 / Chapter 2.3.1 --- Correspondence Extraction --- p.14 / Chapter 2.3.2 --- Self-Calibration --- p.17 / Chapter 2.3.3 --- Extrinsic Parameters Estimation --- p.17 / Chapter 2.3.4 --- Scene Point Coordinates Computation --- p.18 / Chapter 2.3.5 --- Bundle Adjustment --- p.19 / Chapter 2.4 --- Object Assimilation --- p.19 / Chapter 2.5 --- Summary --- p.21 / Chapter Chapter 3. --- Camera Calibration --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- Chapter Organization --- p.22 / Chapter 3.3 --- Brief Review of Camera Calibration --- p.23 / Chapter 3.4 --- Camera Intrinsic Parameters --- p.23 / Chapter 3.5 --- Difficulty of the Calibration Problem --- p.25 / Chapter 3.6 --- Non-automatic Calibration --- p.26 / Chapter 3.6.1 --- DLT --- p.26 / Chapter 3.6.2 --- Vanishing Points Approach --- p.26 / Chapter 3.6.3 --- Homography Approach --- p.28 / Chapter 3.7 --- Auto-Calibration --- p.29 / Chapter 3.7.1 --- Square Pixel with Known Principal Points --- p.30 / Chapter 3.7.2 --- Constant Camera Matrices --- p.31 / Chapter 3.8 --- Experiment --- p.33 / Chapter 3.8.1 --- Experimental Measurement --- p.33 / Chapter 3.8.2 --- Experimental Results --- p.34 / Chapter 3.9 --- Conclusion --- p.37 / Chapter Chapter 4. --- Bundle Adjustment --- p.38 / Chapter 4.1 --- Introduction --- p.38 / Chapter 4.2 --- Descent Direction and Gradient Method --- p.39 / Chapter 4.3 --- Problem Implementation --- p.40 / Chapter 4.4 --- Newton Method --- p.40 / Chapter 4.5 --- Gauss-Newton and Levenberg-Marquardt Method --- p.41 / Chapter 4.6 --- Linear Line Search --- p.43 / Chapter 4.7 --- Golden Section [38] --- p.44 / Chapter 4.8 --- Experiment --- p.47 / Chapter 4.9 --- Summary --- p.50 / Chapter Chapter 5. --- Site Reconstruction Review --- p.51 / Chapter 5.1. --- Introduction --- p.51 / Chapter 5.2. --- Chapter Organization --- p.51 / Chapter 5.3. --- Road Reconstruction --- p.51 / Chapter 5.4. --- Cliff Reconstruction --- p.54 / Chapter 5.5. --- Building Reconstruction --- p.56 / Chapter 5.6. --- Object Assimilation --- p.60 / Chapter 5.7. --- Gallery --- p.61 / Chapter 5.8. --- Application --- p.64 / Chapter Chapter 6. --- Conformal Point Theory --- p.65 / Chapter 6.1. --- Introduction --- p.65 / Chapter 6.2. --- Chapter Organization --- p.65 / Chapter 6.3. --- Hartley Conformal Point Theory --- p.66 / Chapter 6.3.1 --- Angle Measurement Making Use of the Conformal Point --- p.66 / Chapter 6.3.2 --- Position of the Conformal Point --- p.66 / Chapter 6.3.3 --- Proof of the Metric Measurement with the Conformal Point --- p.67 / Chapter 6.3.4 --- Limitation of Hartley's Theory --- p.69 / Chapter 6.4. --- The Discovery of Vanishing Line from 2 or More Images --- p.69 / Chapter 6.4.1 --- Parallax and Plane Stabilization --- p.70 / Chapter 6.4.2 --- Recovery of Vanishing Point by Ideal Plane Stabilization --- p.71 / Chapter 6.5 --- Determining the Infinite Homography and Angle Measurement --- p.73 / Chapter 6.5.1 --- "Four Corresponding Vanishing Points, 3 of which are of Orthogonal Directions" --- p.73 / Chapter 6.5.2 --- "Three Corresponding Orthogonal Point Pairs, and Known Epipoles" --- p.74 / Chapter 6.5.3 --- Known camera matrix and Four Distant Points --- p.74 / Chapter 6.6 --- Applications --- p.77 / Chapter 6.7 --- Conclusion --- p.77 / Chapter 6.8 --- Notes on Publication --- p.78 / Chapter Chapter 7. --- Conclusions --- p.79 / Chapter 7.1 --- Summary --- p.79 / Chapter 7.2 --- Conclusion and Future Work --- p.80 / Appendix A. References --- p.82 / Appendix B. Experiment Dataset --- p.86 / Chapter B.1. --- Introduction --- p.86 / Chapter B.2. --- Synthetic Dataset 1 (S1) --- p.87 / Chapter B.3. --- Synthetic Dataset 2 (S2) --- p.89 / Chapter B.4. --- Real Dataset 1 (Rl) --- p.91 / Chapter B.5. --- Real Dataset 2 (R2) --- p.92 / Chapter B.6. --- Real Dataset 3 (R3) --- p.93 / Appendix C. Mathematical Proof of Vanishing Line Detection by Infinite Plane Stabilization --- p.94

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis (M.Sc.)--York University, 2004. Graduate Programme in Geography. / Typescript. Includes bibliographical references (leaves 98-103). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99353

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Transposable elements (TEs) are utilized for the creation of a wide range of transgenic organisms. However, in some systems, this technique is not very efficient due to low transposition frequencies and integration into unstable or transcriptionally inactive genomic regions. One approach to ameliorate this problem is to increase knowledge of how transposons move and where they integrate into target genomes. Most transposons do not insert randomly into their host genome, with class II TEs utilizing target sequences of between 2 – 8 bp in length, which are duplicated upon insertion. Furthermore, amongst insertion sites, certain sites are preferred for insertion and hence are classified as hot spots, while others not targeted by TEs are referred to as cold spots. The hypothesis tested in this analysis is that in addition to the primary consensus target sequence, secondary and tertiary DNA structures have a significant influence on TE target site preference. Bioinformatics was used to predict and analyze the structure of the flanking DNA around known insertion sites and cold spots for various TEs, to understand why insertion sites are used preferentially to cold spots for element integration. Hidden Markov Models were modeled and trained to analyze datasets of insertions of the P element in the Drosophila melanogaster genome, the Tc1 element in the Caenorhabditis elegans genome, and insertions of the Mos1, piggyBac and Hermes transposons into the target plasmid pGDV1. Analysis of the DNA structural profiles of the insertion sites for the P element and Hermes transposons revealed that both transposons targeted regions of DNA with a relatively high degree of bendability/flexibility at the insertion site. However, similar trends were not observed for the Tc1, Mos1 or piggyBac transposons. Hence, it is believed that the secondary structural features of DNA can contribute to target site preference for some, but not all transposable elements.