Input information maximization for improving self-organizing maps

In this paper, we propose a new type of information-theoretic method called “input information maximization” to improve the performance of self-organizing maps. We consider outputs from input neurons by focusing on winning neurons. The outputs are based on the difference between input neurons and the corresponding winning neurons. Then, we compute the uncertainty of input neurons by normalizing the outputs. Input information is defined as a decrease in the uncertainty of input neurons from a maximum and observed value. When input information increases, fewer input neurons tend to be activated. In the maximum state, only one neuron is on, and all others are off. We applied the method to two data sets, namely, the Senate and voting attitude data sets. In both, experimental results confirmed that when input information increased, quantization and topographic errors decreased. In addition, clearer class structure could be extracted by increasing input information. In comparison to our previous methods to detect the importance of input neurons, the present method turned out to be good at producing faithful representations with much more simplified computational procedures.     

Understanding the local reality of the adoption of sustainable practices and farmer livelihoods: the case of pummelo farming in Chaiyaphum, Northeast Thailand

In the light of the serious economic crisis of 1997 and Agenda 21, as proposed in the United Nations Conference on Environment and Development in 1992, the Thai government has increased commitments to sustainable agricultural development. This paper examines how local Thai farmers are interacting with the policy imperatives of sustainable agriculture, focusing on the adoption of sustainable practices and farmer livelihoods. Two pummelo producing communities in Chaiyaphum province, Northeast Thailand were chosen as examples and compared: one, located in the highlands, focused on sales for local and domestic markets and the other, located in the lowlands, included sales for export. Both were involved in the implementation of Q-GAP, a public policy for good agricultural practices. The pummelo producers discursively sought the best combination of conventional and sustainable farming practices by balancing the benefits and risks concerning labour requirements, commercial results, financial risks associated with credit loans and investment and ecological sustainability.     

Flexible feature discovery and structural information control

In this paper, we propose a new information theoretic method called structural information control for flexible feature discovery. The new method has three distinctive characteristics, which traditional competitive learning fails to offer. First, the new method can directly control competitive unit activation patterns, whereas traditional competitive learning does not have any means to control them. Thus, with the new method, it is possible to extract salient features not discovered by traditional methods. Second, competitive units compete witheach other by maximizing their information content about input patterns. Consequently, this information maximization makes it possible to control flexibly competition processes. Third, in structural information control, it is possible to define many different kinds of information content, and we can choose a specific type of information according to a given objective. When applied to competitive learning, structural information can be used to control the number of dead or spare units, and to extract macro as well as micro features of input patterns in explicit ways. We first applied this method to simple pattern classification to demonstrate that information can be controlled and that different neuron firing patterns can be generated. Second, a dipole problem was used to show that structural information could provide representations similar to those by the conventional competitive learning methods. Finally, we applied the method to a language acquisition problem in which networks must flexibly discover some linguistic rules by changing structural information. Especially, we attempted to examine the effect of the information parameter to control the number of dead neurons, and thus to examine how macro and micro features in input patterns can explicitly be discovered by structural information.     

Wien2wannier: From linearized augmented plane waves to maximally localized Wannier functions

We present an implementation of an interface between the full-potential linearized augmented plane wave package Wien2k and the wannier90 code for the construction of maximally localized Wannier functions. The FORTRAN code and a documentation is made available and results are discussed for SrVO₃, Sr₂IrO₄ (including spin-orbit coupling), LaFeAsO, and FeSb₂.     

Enhancement of the thermal stability and mechanical properties of a PMMA/aluminum trihydroxide composite synthesized via bead milling

The use of aluminum trihydroxide (ATH) fillers as non-halogen flame retardants for polymethylmethacrylates (PMMA) creates a conflict between the mechanical properties and heat resistance of the composites. Therefore, to ensure that the PMMA mechanical properties remain satisfactory, improvements in both the filler–polymer interactions and the ability to control the size and size distribution, morphology and dispersion of the fillers are required. Thus, in the present study, bead milling was used to control both the size distribution and dispersion of ATH fillers in MMA, which had an initial average size of 0.75 μm. The dispersion was obtained by alteration of the surface characteristics of ATH fillers using a silane-based dispersing agent, (3-acryloxypropyl) trimethoxysilane (APTMS). Bead milling successfully comminuted the ATH particles and prevented the formation of ATH agglomerates. The smallest average size of the ATH particles after bead milling was 300 nm. Highly dispersed ATH filler particles were observed in the TEM images of the PMMA/ATH composites. The filler–polymer interaction, i.e. the interaction parameter (B), was calculated. The effects of volume fraction, particle size distribution, and surface modification of the fillers on the results of dynamic mechanical analysis (DMA) are discussed. The thermal stability of the PMMA/ATH composites was also investigated using thermal gravimetric analysis (TGA).     

Improving feature extraction performance of greedy network-growing algorithm by inverse euclidean distance

We propose here a new computational method for the information-theoretic method, called the greedy network-growing algorithm, to facilitate a process of information acquisition. We have so far used the sigmoidal activation function for competitive unit outputs. The method can effectively suppress many competitive units by generating strongly negative connections. However, because methods with the sigmoidal activation function are not very sensitive to input patterns, we have observed that in some cases final representations obtained by the method do not necessarily faithfully describe input patterns. To remedy this shortcoming, we employ the inverse of distance between input patterns and connection weights for competitive unit outputs. As the distance becomes smaller, competitive units are more strongly activated. Thus, winning units tend to represent input patterns more faithfully than in the previous method with the sigmoidal activation function. We applied the new method to artificial data analysis and animal classification. Experimental results confirmed that more information can be acquired and more explicit features can be extracted by our new method.     

Biomimetic Zirconia with Cactus-Inspired Meso-Scale Spikes and Nano-Trabeculae for Enhanced Bone Integration

Biomimetic design provides novel opportunities for enhancing and functionalizing biomaterials. Here we created a zirconia surface with cactus-inspired meso-scale spikes and bone-inspired nano-scale trabecular architecture and examined its biological activity in bone generation and integration. Crisscrossing laser etching successfully engraved 60 μm wide, cactus-inspired spikes on yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) with 200–300 nm trabecular bone-inspired interwoven structures on the entire surface. The height of the spikes was varied from 20 to 80 μm for optimization. Average roughness (Sa) increased from 0.10 μm (polished smooth surface) to 18.14 μm (80 μm-high spikes), while the surface area increased by up to 4.43 times. The measured dimensions of the spikes almost perfectly correlated with their estimated dimensions (R² = 0.998). The dimensional error of forming the architecture was 1% as a coefficient of variation. Bone marrow-derived osteoblasts were cultured on a polished surface and on meso- and nano-scale hybrid textured surfaces with different spike heights. The osteoblastic differentiation was significantly promoted on the hybrid-textured surfaces compared with the polished surface, and among them the hybrid-textured surface with 40 μm-high spikes showed unparalleled performance. In vivo bone-implant integration also peaked when the hybrid-textured surface had 40 μm-high spikes. The relationships between the spike height and measures of osteoblast differentiation and the strength of bone and implant integration were non-linear. The controllable creation of meso- and nano-scale hybrid biomimetic surfaces established in this study may provide a novel technological platform and design strategy for future development of biomaterial surfaces to improve bone integration and regeneration.     

Improving visualisation and prediction performance of supervised self-organising map by modified contradiction resolution

We have previously proposed a new type of information-theoretic method where a neuron is evaluated by itself (self-evaluation) and by its surrounding neurons (outer-evaluation). If contradiction between different types of evaluation exists, it is reduced as much as possible. In the present paper, we try to separate self- and outer-evaluation more explicitly and introduce the importance of neurons. First, we separate self- and outer-evaluation to enhance the characteristics shared by the two types of evaluation. Second, we introduce the importance of neurons in evaluation. By using a limited number of important neurons in evaluation, we expect the main characteristics in input patterns to emerge. We applied this contradiction resolution to two types of data, namely, the Senate data and the Euro-yen exchange rates. In both data sets, experimental results confirmed that improved prediction performance was obtained. Prediction performance was better than that obtained by the conventional self-organising map (SOM) and radial basis function networks. In addition, final representations obtained by contradiction resolution were easier to interpret than those given by the conventional SOM. Experimental results confirmed that improved interpretation and visualisation were accompanied by improved prediction performance.     

Photo-induced valence tautomerism in Co complexes

A number of photofunctional molecular compounds have been developed recently. Typical examples of these are phototunable valence tautomeric compounds, which are now attracting great attention. When the charge-transfer bands of some Co valence tautomeric compounds are excited at low temperature, metastable redox isomers can be created after irradiation. The lifetimes of the metastable states can be more than several hours. These transformations can involve changes in the magnetic properties of the compounds, as well as their color. Hence, these compounds can be regarded as novel photomagnetic materials. The photoresponsive behaviors of these valence tautomeric compounds are similar to those of spin-crossover complexes (light-induced excited spin-state trapping effects).