Animating Pictures of Fluid using Video Examples

We propose a system that allows the user to design a continuous flow animation starting from a still fluid image. The basic idea is to apply the fluid motion extracted from a video example to the target image. The system first decomposes the video example into three components, an average image, a flow field and residuals. The user then specifies equivalent information over the target image. The user manually paints the rough flow field, and the system automatically refines it using the estimated gradients of the target image. The user semi-automatically transfers the residuals onto the target image. The system then approximates the average image and synthesizes an animation on the target image by adding the transferred residuals and warping them according to the user-specified flow field. Finally, the system adjusts the appearance of the resulting animation by applying histogram matching. We designed animations of various pictures, such as rivers, waterfalls, fires, and smoke.     

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.     

Bottom-up pyramid cellular acceptors with three-dimensional layers

In 1997, C.R. Dyer and A. Rosenfeld introduced an acceptor on a two-dimensional pattern (or tape), called the pyramid cellular acceptor, and demonstrated that many useful recognition tasks are executed by pyramid cellular acceptors in time proportional to the logarithm of the diameter of the input. They also introduced a bottom-up pyramid cellular acceptor which is a restricted version of the pyramid cellular acceptor, and proposed some interesting open problems of the bottom-up pyramid cellular acceptors. On the other hand, we think that the study of threedimensional automata has been meaningful as the computational model of three-dimensional information processing such as computer vision, robotics, and so forth. In this paper, we investigate about bottom-up pyramid cellular acceptors with three-dimensional layers, and show their some accepting powers. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

An application of the 2-nitrobenzenesulfenyl method to proteomic profiling of human colorectal carcinoma: A novel approach for biomarker discovery

In the development of novel biomarkers, the proteomic approach is advantageous because using it the cancer-associated proteins can be directly identified. We previously developed a 2-nitrobenzenesulfenyl (NBS) method to improve quantitative proteome analysis. Here, we applied this method to proteomic profiling of colorectal carcinoma (CRC) to identify novel proteins with altered expression in CRC. Each pair of tumor and normal tissue specimens from 12 CRC patients was analyzed, and approximately 5000 NBS-labeled paired peaks were quantified. Peaks with altered signal intensities (>1.5-fold) and occurring frequently in the samples (>70%) were selected, and 128 proteins were identified by MS/MS analyses as differentially expressed proteins in CRC tissues. Many proteins were newly revealed to be CRC related; 30 were reported in earlier studies of CRC. Six proteins that were up-regulated in CRC (ZYX, RAN, RCN1, AHCY, LGALS1, and VIM) were further characterized and validated by Western blot and immunohistochemistry. All six were found to be CRC-localized, either in cancer cells or in stroma cells near the cancer cells. These results indicate that the proteins identified in this study are novel candidates for CRC markers, and that the NBS method is useful in proteome mining to discover novel biomarkers.     

Role of granulosa and theca cell interactions in ovarian follicular maturation

We developed a culture system in which two types of ovarian follicular cells were allowed to attach to opposite sides of a collagen membrane. Using this in vitro cell culture system, we studied the effects of granulosa- and theca-cell interaction on the morphology, structure, and function of bovine ovarian follicular cells. In the first part of the study, we explored how the interaction between theca and granulosa cells affects the morphology and structure of the cells. This study was done using follicular cells collected from bovine ovarian follicles at the early developmental stage. Granulosa cells cultured alone were flattened, and formed a monolayer sheet. By contrast, granulosa cells cultured with theca cells were convex, and formed multilayer sheets. Theca cells cultured alone were thin, flat, and spindle-shaped. Theca cells cultured with granulosa cells were also spindle-shaped; however, they appeared convex and more densely packed when compared with theca cells cultured alone. In the second part of the study, the possible role of the cellular interaction in the control of differentiation and growth of granulosa and theca cells was investigated. When follicular cells were isolated from the early stage of follicular development, theca cells reduced progesterone and inhibin production by granulosa cells and augmented the growth of granulosa cells. When the cells were isolated from the late stage of follicular development, by contrast, theca cells augmented hormonal production by granulosa cells, and did not affect the growth of granulosa cells. The growth and androstenedione production by theca cells were increased by the presence of granulosa cells, irrespective of the origin of follicular cells. These results demonstrated that communication between two types of follicular cells results in reciprocal modulation of their morphology, structure, growth, and function. Cellular interactions seem to be one of the major factors controlling the differentiation and growth of the follicular cells during the follicular maturation process. In contrast to granulosa and theca cells cultured alone, cells in the coculture seemed to possess morphological and functional characteristics more similar to those of cells in the growing follicular wall in vivo. Thus, we speculate that the interaction between these two types of follicular cells is essential for the maintenance of original structure and function of the bovine follicular wall.     

Doing Business with Theory: Communities of Practice in Knowledge Management

We explore how the notion of communities of practice (CoPs) was translated and popularized from its original inception by Lave and Wenger in 1991. We argue that the Institute for Research on Learning (IRL), a spin-off of Xerox PARC, proved instrumental in enrolling CoPs into the knowledge management (KM) discipline. IRL objectified, packaged, and made a business out of CoPs. CoPs in KM are now a formalized process coupled with technological artifacts to build groups of people who effectively share knowledge across boundaries. Drawing from participant observations, archival documents, and interviews with KM practitioners in the aerospace industry as well as key players of IRL, our research seeks to unveil the invisible history that the popularization of a theory can often obscure. We argue that CoPs provide a case study for understanding how abstract concepts in science are strategically and subconsciously reified, or made objects of inquiry, and appropriated by actors. This reification of a “soft” science blurs the line between theory and technology.     

Copolymerization of Vinyl Chloride and Ethylene with Cp<Superscript>*</Superscript>Ti( OCH<Subscript>3</Subscript>)<Subscript>3</Subscript>/MAO Catalyst

Summary Copolymerization of vinyl chloride (VC) and ethylene with Cp^*Ti( OCH₃)₃/MAO catalyst was investigated. The Cp^*Ti( OCH₃)₃/MAO catalyst initiated the copolymerization of VC with ethylene, although the copolymer yields were low. In the ¹³C NMR spectra of the copolymers, the peaks based on junction part between VC and ethylene was observed, but the signals were small. From DSC measurement of the copolymers, only one glass transition temperature was observed. Thus, it is clear that the copolymerization with Cp^*Ti( OCH₃)₃/MAO catalysts gave the copolymer, and the copolymer consisting of block sequence rather than random copolymer. Received: 13 November 2002/Revised: 6 January 2003/Accepted: 10 January 2003 Correspondence to Kiyoshi Endo     

Multi-modal Information Integration for Interactive Multi-agent Systems

This paper proposes a multi-agent system that carries out cooperative work. To achieve this, we use Fuzzy Associative Memory Organizing Unit Systems (FAMOUS) and Conceptual Fuzzy Sets (CFS). By using these proposed methods, each agent robot can decide its own behaviour to suit the situation in its environment. We apply this system to an Intelligent Transportation System (ITS) and do simulations.     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.