KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

Controlled polarization rotation of an optical field in multi-Zeeman-sublevel atoms

We investigate, both theoretically and experimentally, the phenomenon of polarization rotation of a weak, linearly-polarized optical (probe) field in an atomic system with multiple three-level electromagnetically induced transparency (EIT) sub-systems. The polarization rotation angle can be controlled by a circularly-polarized coupling beam, which breaks the symmetry in number of EIT subsystems seen by the left- and right-circularly-polarized components of the weak probe beam. A large polarization rotation angle (up to 45 degrees) has been achieved with a coupling beam power of only 15 mW. Detailed theoretical analyses including different transition probabilities in different transitions and Doppler-broadening are presented and the results are in good agreements with the experimentally measured results.Comment: 28pages, 12figure

Gallium/aluminum interdiffusion between n-GaN and sapphire

The distribution profiles of Ga and Al near the interface of the n-GaN/sapphire system were measured by x-ray energy dispersive spectroscopy (XEDS). The results are obtained by the corrected XED spectra. First, the gallium diffusing into the sapphire substrate obeys the law of remainder probability function. The gallium diffusion coefficient DGa=2.30×10-13 cm2s-1 was calculated by theoretical fitting. Second, the diffusion is associated with the GaN growth process at high temperature. Compared to the diffusion of Ga into the sapphire substrate, much less Al antidiffusion from the substrate to the GaN film, with diffusion coefficient DA1 approximately equal to 4.8×10-15 cm2s-1, was observed in the film. © 1998 American Institute of Physics.published_or_final_versio

Nanofibers and nanoparticles from the insect-capturing adhesive of the Sundew (Drosera) for cell attachment

<p>Abstract</p> <p>Background</p> <p>The search for naturally occurring nanocomposites with diverse properties for tissue engineering has been a major interest for biomaterial research. In this study, we investigated a nanofiber and nanoparticle based nanocomposite secreted from an insect-capturing plant, the Sundew, for cell attachment. The adhesive nanocomposite has demonstrated high biocompatibility and is ready to be used with minimal preparation.</p> <p>Results</p> <p>Atomic force microscopy (AFM) conducted on the adhesive from three species of Sundew found that a network of nanofibers and nanoparticles with various sizes existed independent of the coated surface. AFM and light microscopy confirmed that the pattern of nanofibers corresponded to Alcian Blue staining for polysaccharide. Transmission electron microscopy identified a low abundance of nanoparticles in different pattern form AFM observations. In addition, energy-dispersive X-ray spectroscopy revealed the presence of Ca, Mg, and Cl, common components of biological salts. Study of the material properties of the adhesive yielded high viscoelasticity from the liquid adhesive, with reduced elasticity observed in the dried adhesive. The ability of PC12 neuron-like cells to attach and grow on the network of nanofibers created from the dried adhesive demonstrated the potential of this network to be used in tissue engineering, and other biomedical applications.</p> <p>Conclusions</p> <p>This discovery demonstrates how a naturally occurring nanofiber and nanoparticle based nanocomposite from the adhesive of Sundew can be used for tissue engineering, and opens the possibility for further examination of natural plant adhesives for biomedical applications.</p

Nanofibers and nanoparticles from the insect-capturing adhesive of the Sundew (\u3cem\u3eDrosera\u3c/em\u3e) for cell attachment

Background The search for naturally occurring nanocomposites with diverse properties for tissue engineering has been a major interest for biomaterial research. In this study, we investigated a nanofiber and nanoparticle based nanocomposite secreted from an insect-capturing plant, the Sundew, for cell attachment. The adhesive nanocomposite has demonstrated high biocompatibility and is ready to be used with minimal preparation. Results Atomic force microscopy (AFM) conducted on the adhesive from three species of Sundew found that a network of nanofibers and nanoparticles with various sizes existed independent of the coated surface. AFM and light microscopy confirmed that the pattern of nanofibers corresponded to Alcian Blue staining for polysaccharide. Transmission electron microscopy identified a low abundance of nanoparticles in different pattern form AFM observations. In addition, energy-dispersive X-ray spectroscopy revealed the presence of Ca, Mg, and Cl, common components of biological salts. Study of the material properties of the adhesive yielded high viscoelasticity from the liquid adhesive, with reduced elasticity observed in the dried adhesive. The ability of PC12 neuron-like cells to attach and grow on the network of nanofibers created from the dried adhesive demonstrated the potential of this network to be used in tissue engineering, and other biomedical applications. Conclusions This discovery demonstrates how a naturally occurring nanofiber and nanoparticle based nanocomposite from the adhesive of Sundew can be used for tissue engineering, and opens the possibility for further examination of natural plant adhesives for biomedical applications

Feature Boosting Network For 3D Pose Estimation

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.Comment: Accepted to T-PAMI. DOI: 10.1109/TPAMI.2019.289442

Face detection using deep learning: An improved faster RCNN approach

In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark