Lighting simulation is very important in realistic image synthesis, and the simulation of subsurface scattering has recently attracted much attention. Although the dipole/multipole model has succeeded in creating realistic images, it is still difficult to deal with volumetric features in subsurface scattering, which is important when rendering optically thin objects. This paper proposes a novel rendering method that utilizes the plane-parallel solution and the ray-marching method. The ray-marching method has been used to calculate single scattering solutions, and the plane-parallel solution has been adopted to calculate BRDFs. By combining these techniques, the proposed method efficiently captures volumetric features in multiple subsurface scattering events. In our experiments, the proposed method demonstrated a performance superior to that of previous methods in terms of accuracy.

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Augmenting the global ranking based on the linkage structure of the Web is one of the popular approaches in data engineering community today for enhancing the search and ranking quality of Web information systems. This is typically done through automated learning of user interests and re-ranking of search results through semantic based personalization. In this paper, we propose a query context window (QCW) based framework for Selective uTilization of search history in personalized leArning and re-Ranking (STAR). We conduct extensive experiments to compare our STAR approach with the popular directory-based search methods (e.g., Google Directory search) and the general model of most existing re-ranking schemes of personalized search. Our experimental results show that the proposed STAR framework can effectively capture user-specific query-dependent personalization and improve the accuracy of personalized search over existing approaches.

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We describe an improved way of estimating parameters for an integrated weighted-mixture model consisting of both harmonic and inharmonic tone models. Our final goal is to build an instrument equalizer (music remixer) that enables a user to change the volume of parts of polyphonic sound mixtures. To realize the instrument equalizer, musical signals must be separated into each musical instrument part. We have developed a score-informed sound source separation method using the integrated model. A remaining but critical problem is to find a way to deal with timbre varieties caused by various performance styles and instrument bodies because our method used template sounds to represent their timbre. Template sounds are generated from a MIDI tone generator based on an aligned score. Difference of instrument bodies between mixed signals and template sounds causes timbre difference and decreases separation performance. To solve this problem, we train probabilistic distributions of timbre features using various sounds to reduce template dependency. By adding a new constraint of maximizing the likelihood of timbre features extracted from each tone model, we can estimate model parameters that express the timbre more accurately. Experimental results show that separation performance improved from 4.89 to 8.48dB.

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Scientific researchers need support for their information gathering from the Web, because the growth of Internet accessibility raises the problem of Internet information overload. Social Bookmarking Service (SBS) is a promising technology to solve the problem by the benefit of collaborative information gathering. The paper describes the design and evaluation of a novel function of SBS to foster collaborative information gathering by providing mutual awareness information about browsing behaviors of SBS users. This information increases the probabilities of discovering the useful information by recommending the potential collaborators to the user. A case study on an experimental SBS was performed to evaluate the feasibility of the mutual awareness information for individuals and research communities. In order to verify the validity of the design quantitatively, an experiment was conducted using agent-based simulation based on an extension of the SIR model for epidemics. The results, either from the case study or the agent-based simulation, argue the effectiveness of the proposed function to provide mutual awareness information for fostering collaborative information gathering in SBS.

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