Within-class multimodal classification

Multimedia Tools and Applications - In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is...     

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.     

The effect of optimized carbon source on the synthesis and composition of exopolysaccharides produced by Lactobacillus paracasei

This study aimed to predict the optimal carbon source for higher production of exopolysaccharides (EPS) by Lactobacillus paracasei TD 062, and to evaluate the effect of this carbon source on the production and monosaccharide composition of EPS. We evaluated the EPS production capacity of 20 strains of L. paracasei under the same conditions. We further investigated L. paracasei TD 062, which showed the highest EPS-producing activity (0.609 g/L), by examining the associated biosynthesis pathways for EPS. Genomics revealed that fructose, mannose, trehalose, glucose, galactose, and lactose were carbon sources that L. paracasei TD 062 could use to produce EPS. We identified an EPS synthesis gene cluster that could participate in transport, export, and sugar chain synthesis, and generate 6 sugar nucleotides. Experimental results showed that the sugar content of the EPS produced using fermentation with the optimized carbon source (fructose, mannose, trehalose, glucose, galactose, and lactose) increased by 115%. Furthermore, use of the optimized carbon source changed the monosaccharide content of the associated EPS. The results of enzyme activity measurements showed significant increases in the activity of 2 key enzymes involved in the glycoside synthesis pathway. Our study revealed that optimizing the carbon source provided for fermentation not only increased the production of EPS, but also affected the composition of the monosaccharides by increasing enzyme activity in the underlying synthesis pathways, suggesting an important role for carbon source in the production of EPS by L. paracasei TD 062.     

Role of Co in formation of Ni-Ti clusters in maraging stainless steel

The effect of Co addition on the formation of Ni-Ti clusters in maraging stainless steel was studied by three dimensional atom probe (3DAP) and first-principles calculation. The cluster analysis based on the maximum separation approach showed an increase in size but a decrease in density of Ni-Ti clusters with increasing the Co content. The first-principles calculation indicated weaker Co-Ni (Co-Ti) interactions than Co-Ti (Fe-Ti) interactions, which should be the essential reason for the change of distribution characteristics of Ni-Ti clusters in bcc Fe caused by Co addition.     

Surface structural and solar absorptance features of nitrate-based copper-cobalt oxides composite coatings: Experimental studies and molecular dynamic simulation

The copper and cobalt oxides composites coatings on aluminum substrates have been successfully synthesized via sol-gel method using nitrate-based sol precursors. The composites were characterized by X-ray Diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Field Emission Scanning Electron Microscopy (FESEM), Atomic Force Microscopy (AFM), and UV–Vis–NIR spectrophotometry. The sol-gel reactions were discussed and Molecular Dynamics (MD) simulation was integrated into the study to predict molecules assembly properties. The XRD analyses revealed that the CuO and the Co₃O₄ composites were formed after the annealing process with the average difference of the calculated lattice parameters compared to ICDDs was 1.17%. The surface electronic structure was mainly consisted of tetrahedral Cu(I), octahedral Cu(II), tetrahedral Co(II), octahedral Co(III) as well as surface, sub-surface and lattice oxygen O^?. The XRD, XPS and MD simulation results showed that there was minimal (or possibly non-existing) indication of copper-cobalt mixed phase oxides formations. FESEM and AFM surveys revealed that the coating had a porous surface composed of interlinked nanoparticles in the range of ~?10 to ~?40?nm. UV–Vis–NIR reflectance spectra showed that the sol precursors concentration and the dip-drying cycle significantly influenced the absorptance value with optimum absorptance (α) of 88.7% exhibited by coating synthesized using sol concentration of 0.1?M and 10 dip-drying cycles. High absorptance value and simplicity in the synthesis process render the coatings to be very promising candidates for solar selective absorber (SSA) applications.     

Multi-objective layout optimization for an orbital propellant depot

Structural and Multidisciplinary Optimization - The overall layout optimization design of an orbital propellant depot involves the optimization of shape, size, and positions of propellant tanks in...     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.