62684482
Relevant linear feature extraction using side-information and unlabeled data
"Learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant linear feature extraction. It efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. Both good relevant feature extraction and reasonable similarity matrix estimation can be realized. Specifically, we adopt relevant component analysis (RCA) under this framework and get the derived iterative self-enhanced relevant component analysis (ISERCA) algorithm. The experimental results on several data sets show that ISERCA outperforms RCA.
12875596
Exploring patterns of social commonality among file directories at work
We studied files stored by members of a work organization for patterns of social commonality. Discovering identical or similar documents, applications, developer libraries, or other files may suggest shared interests or experience among users. Examining actual file data revealed a number of individual and aggregate practices around file storage. For example, pairs of users typically have many (over 13,000) files in common. A prototype called LiveWire exploits this commonality to make file backup and restore more efficient for a work organization. We removed commonly shared files and focused on specific filetypes that represent user activity to find more meaningful files in common. The Consolidarity project explores how patterns of file commonality could encourage social networking in an organizational context. Mechanisms for addressing the privacy concerns raised by this approach are discussed.
46355986
GIS-based simulation as an exploratory analysis for space-time processes
Abstract. The purpose of this paper is to argue for a novel use of geographical information system (GIS) as an exploratory device for understanding complex space-time processes. Conventionally, a GIS has been configured as a spatial database management system. Therefore, the capabilities of data input, storage, retrieval, manipulation and display have been well developed, whereas the analytical and dynamic processing and modelling capabilities are under-developed. The importance of integrating GIS with dynamic and analytical models has been widely recognised. Extending the use of GIS into spatial simulation represents a plausible research direction. On the other hand, the usefulness of conventional deductive model becomes limited when dealing with complex space-time processes. In essence, the inherent complexity calls for a simulation approach. The new nonlinear modelling paradigm, for example, cellular automata (CA), opened a way in which behaviourally-richer dynamics and more micro-spatial data could be incorporated into understanding space-time processes. It is argued therefore that GIS can be used as a platform – a controlled environment or laboratory – for exploring complex space-time processes. The discussion of GIS-based simulation in this paper is illustrated with a primitive simulation of the evolution of urban spatial structure.
17976333
Advances in quantification and characterization of telomerase activity by the telomeric repeat amplification protocol (TRAP)
The telomeric repeat amplification protocol (TRAP) assay has been used to test telomerase activity in numerous cancer specimens. We describe primers, controls and quantification methods for the TRAP assay to accurately measure the level of telomerase activity in clinical samples. The assay is reliable and reproducible in routine analyses and can be used to estimate the processivity of telomerase activity.
3092695
Multiresolution Graphics on Ubiquitous Displays Using Wavelets
Large meshes or images cannot be rendered at full resolution on mobile devices such as cell phones, PDAs and laptops since these devices have limited storage, CPU, memory, display, and battery power. Wavelet-based multiresolution analysis can represent meshes and graphics input at multiple Levels of Detail (LODs). We propose UbiWave, a wavelet-based framework for scalable transmission of large meshes and graphics content to heterogeneous ubiquitous computing devices. In UbiWave, a base representation and different levels of wavelet coefficients are pre-generated at the server. An optimal LOD level is transmitted to each mobile client based on its spacification and wireless channel conditions, where the corresponding LOD is reconstructed. To save scarce resources on mobile devices, we render graphics content at the lowest LOD that does not show visual artifacts, called the point of indiscernability (PoI). By rendering content at the PoI instead of the highest resolution, we are able save 61% decode time and 45% energy usage on the client.