The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

Hierarchical, ultrathin single-crystal nanowires of CdS conveniently produced in laser-induced thermal field

Hierarchical nanowires (HNWs) exhibit unique properties and have wide applications, while often suffering from imperfect structure. Herein, we report a facile strategy toward ultrathin CdS HNWs with monocrystal structure, where a continuous-wave (CW) Nd:YAG laser is employed to irradiate an oleic acid (OA) solution containing precursors and a light absorber. The high heating rate and large temperature gradient generated by the CW laser lead to the rapid formation of tiny zinc-blende CdS nanocrystals which then line up into nanowires with the help of OA molecules. Next, the nanowires experience a phase transformation from zinc-blende to wurtzite structure, and the transformation-induced stress creates terraces on their surface, which promotes the growth of side branches and eventually results in monocrystal HNWs with an ultrathin diameter of 24 nm. The one-step synthesis of HNWs is conducted in air and completes in just 40 s, thus being very simple and rapid. The prepared CdS HNWs display photocatalytic performance superior to their nanoparticle counterparts, thus showing promise for catalytic applications in the future.

An adaptive hierarchical approach to the extraction of high resolution medial surfaces

We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octree-based approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments. © 2012 IEEE.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Facile synthesis of hierarchical Cu2O nanocubes as visible light photocatalysts

Hierarchically structured Cu2O nanocubes have been synthesized by a facile and cost-effective one-pot, solution phase process. Self-assembly of 5 nm Cu2O nanocrystallites induced through reduction by glucose affords a mesoporous 375 nm cubic architecture with superior visible light photocatalytic performance in both methylene blue dye degradation and hydrogen production from water than conventional non-porous analogues. Hierarchical nanocubes offer improved accessible surface active sites and optical/electronic properties, which act in concert to confer 200–300% rate-enhancements for the photocatalytic decomposition of organic pollutants and solar fuels.

Hierarchical mesoporous Pd/ZSM-5 for the selective catalytic hydrodeoxygenation of m-cresol to methylcyclohexane

Mesopore incorporation into ZSM-5 enhances the dispersion of Pd nanoparticles throughout the hierarchical framework, significantly accelerating m-cresol conversion relative to a conventional microporous ZSM-5, and dramatically increasing selectivity towards the desired methylcyclohexane deoxygenated product. Increasing the acid site density further promotes m-cresol conversion and methylcyclohexane selectivity through efficient dehydration of the intermediate methylcyclohexanol.

Template-free and eco-friendly synthesis of hierarchical Ag3PO4 microcrystals with sharp corners and edges for enhanced photocatalytic activity under visible light

Herein, we demonstrate a template-free and eco-friendly strategy to synthesize hierarchical Ag3PO4 microcrystals with sharp corners and edges via silver–ammine complex at room temperature. The as-synthesized hierarchical Ag3PO4 microcrystals were characterized by X-ray diffraction, field-emission scanning electron microscope (FESEM), UV–vis diffuse reflectance spectroscopy (UV–vis DRS), BET surface area analyzer, and photoluminescence analysis (PL). Our results clearly indicated that the as-synthesized Ag3PO4 microcrystals possess a hierarchical structure with sharp corners and edges. More attractively, the adsorption ability and visible light photocatalytic activity of the as-synthesized hierarchical Ag3PO4 is much higher than that of conventional Ag3PO4.

Hierarchical ZnO “rod like” architecture synthesized via reverse micellar route for improved photocatalytic activity

Hierarchical ZnO “rod like” architecture was successfully synthesized via reverse micellar route and characterized by various techniques. The FESEM studies show controlled decomposition of zinc oxalate into ZnO “rod like” architecture at 500 °C with slow heat rate at 1°/min. Interestingly, improved photocatalytic activity was observed for the degradation of Rhodamine B, due to the self assembly of hexagonal nanoparticles of zinc oxide forming hierarchical ZnO “rod like” architecture which can greatly enhance the light utilization rate due to its special architecture and enlarge the specific surface area, providing more reaction sites and promoting mass transfer. More importantly, the reusability studies of this architecture were most economical.

Modifying the hierarchical porosity of SBA-15 via mild-detemplation followed by secondary treatments

Fenton-chemistry-based detemplation combined with secondary treatments offers options to tune the hierarchical porosity of SBA-15. This approach has been studied on a series of SBA-15 mesophases and has been compared to the conventional calcination. The as-synthesized and detemplated materials were studied with regard to their template content (TGA, CHN), structure (SAXS, TEM), surface hydroxylation (Blin-Carterets approach), and texture (high-resolution argon physisorption). Fenton detemplation achieves 99% of template removal, leading to highly hydroxylated materials. The structure is better preserved when a secondary treatment is applied after the Fenton oxidation, due to the intense capillary forces during drying in water. Two successful approaches are presented: drying in a low-surface-tension solvent (such as n-BuOH) and a hydrothermal stabilization to further condense the structure and make it structurally more robust. Both approaches give rise to remarkably low structural shrinkage, lower than calcination and the direct water-dried Fenton. Interestingly, the derived textural features are remarkably different. The n-BuOH exchange route gives rise to highly hierarchical structures with enhanced interconnecting pores and the highest surface areas. The hydrothermal stabilization produces large-pore SBA-15 structures with high pore volume, intermediate interconnectivity, and minimal micropores. Therefore, the hierarchical texture can be fine-tuned in these two fashions while the template is removed under mild conditions.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.