Underlayer dependence can be controlled for supercritical fluid deposition (SCFD) of Cu. SCFD, which has a remarkable potential for ultra-conformal deposition and gap-filling, has previously required a metallic underlayer to initiate deposition. Here, this constraint has been overcome by depositing a novel catalytic layer, CuMn x O y , onto a semiconducting and insulating substrate. The stoichiometry of CuMn x O y affected both the morphology of CuMn x O y film and the catalytic effect on succeeding SCFD of Cu. By using this technique of depositing CuMn x O y as a catalytic layer, conformal SCFD of Cu was achieved on high-aspect-ratio trenches (aspect ratio 50) whose surfaces were SiO2. In conclusion, a CuMn x O y film with graded stoichiometry in the depth direction might improve the adhesion between Cu and an insulative underlayer.

The amount of visual information projected from the retina to the brain exceeds the information processing capacity of the latter. Attention, therefore, functions as a filter to highlight important information at multiple stages of the visual pathway that requires further and more detailed analysis. Among other functions, this determines where to fixate since only the fovea allows for high resolution imaging. Visual saliency modeling, i.e. understanding how the brain selects important information to analyze further and to determine where to fixate next, is an important research topic in computational neuroscience and computer vision. Most existing bottom-up saliency models use low-level features such as intensity and color, while some models employ high-level features, like faces. However, little consideration has been given to mid-level features, such as texture, for visual saliency models. In this paper, we extend a biologically plausible proto-object based saliency model by adding simple texture channels which employ nonlinear operations that mimic the processing performed by primate visual cortex. The extended model shows statistically significant improved performance in predicting human fixations compared to the previous model. Comparing the performance of our model with others on publicly available benchmarking datasets, we find that our biologically plausible model matches the performance of other models, even though those were designed entirely for maximal performance with little regard to biological realism.

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Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons' communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat's neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.

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