Search results for: hierarchical pores

Authors: Zhanat Umarova

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Until the present time, modeling of the pores form influence on the hydraulic resistance of membranes and their permeability has not been analyzed. The aim of the given work is the theoretical consideration of the issue on the productivity of polymer membranes with the profile pores and determination of the optimum form of pores.

Keywords: modeling, polymer membranes, permeability, pore’s density

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Deepti Mishra, K. K Pant, Xiu Song Zhao, Muxina Konarova

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Catalytic transformation of simplest hydrocarbon methane into benzene and valuable chemicals over Mo/HZSM-5 has a great economic potential, however, it suffers serious hurdles due to the blockage in the micropores because of extensive coking at high temperature during methane dehydroaromatization (MDA). Under such conditions, it necessitates the design of micro/mesoporous ZSM-5, which has the advantages viz. uniform dispersibility of MoOx species, consequently the formation of active Mo sites in the micro/mesoporous channel and lower carbon deposition because of improved mass transfer rate within the hierarchical pores. In this study, we report a unique strategy to control the porous structures of ZSM-5 through a dual templating approach, utilizing C6 and C12 -surfactants as porogen. DFT studies were carried out to correlate the ZSM-5 framework development using the C6 and C12 surfactants with structure directing agent. The structural and morphological parameters of the synthesized ZSM-5 were explored in detail to determine the crystallinity, porosity, Si/Al ratio, particle shape, size, and acidic strength, which were further correlated with the physicochemical and catalytic properties of Mo modified HZSM-5 catalysts. After Mo incorporation, all the catalysts were tested for MDA reaction. From the activity test, it was observed that C6 surfactant-modified hierarchically porous Mo/HZSM-5(H) showed the highest benzene formation rate (1.5 μmol/gcat. s) and longer catalytic stability up to 270 min of reaction as compared to the conventional microporous Mo/HZSM-5(C). In contrary, C12 surfactant modified Mo/HZSM-5(D) is inferior towards MDA reaction (benzene formation rate: 0.5 μmol/gcat. s). We ascribed that the difference in MDA activity could be due to the hierarchically interconnected meso/microporous feature of Mo/HZSM-5(H) that precludes secondary reaction of coking from benzene and hence contributing substantial stability towards MDA reaction.

Keywords: hierarchical pores, Mo/HZSM-5, methane dehydroaromatization, coke deposition

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Authors: Xin Chen, Moxiao Li, Xuechao Sun, Fei Ti, Shaobao Liu, Feng Xu, Tian Jian Lu

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The composite materials with pores have the characteristics of light weight, sound insulation, and heat insulation, and have broad prospects in many fields, including aerospace. In general, the stiffness of such composite is less than the stiffness of the matrix material, limiting their applications. In this paper, we establish a theoretical model to analyze the deformation mechanism of a nano-pore. The interface between the pores and matrix material is described by the Gurtin-Murdoch model. By considering scale effect related with current deformation, we estimate the effective mechanical properties (e.g., effective shear modulus and bulk modulus) of a composite with nano-pores. Due to the scale effect, the elastic field in the composite was changed and local hardening was observed around the nano-pore, and the effective shear modulus and effective bulk modulus were found to be a function of the surface energy. The effective shear modulus increase with the surface energy and decrease with the size of the nano-pores, and the effective bulk modulus decrease with the surface energy and increase with the size of the nano-pores. These results have potential applications in the nanocomposite mechanics and aerospace field.

Keywords: composite mechanics, nano-inhomogeneity, nano-pores, scale effect

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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Authors: Mustafa Aydın, Ali Murat Sunbul

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Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes.

Keywords: hierarchical linear modeling, nested data, hierarchical structure, data structure

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Authors: Mohammad Alenezi

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Hierarchical nanostructures with higher dimensionality, consisting of nanostructure building blocks such as nanowires, nanotubes, or nanosheets are very attractive. They hold great properties like the high surface-to-volume ratio and well-ordered porous structures, which can be very challenging to attain for other mono-morphological nanostructures. Well-ordered hierarchical nanostructures with high surface-to-volume ratios facilitate gas diffusion into their surfaces as well as scattering of light. Therefore, hierarchical nanostructures are expected to perform highly as gas sensors. A multistage controlled hydrothermal synthesis method to fabricate high-performance single ZnO brushlike hierarchical nanostructure gas sensor from initial nanowires is reported. The performance of the sensor based on brush-like hierarchical nanostructure is analyzed and compared to that of a nanowire gas sensor. The hierarchical gas sensor demonstrated high sensitivity toward low concentration of acetone at high speed of response. The enhancement in the hierarchical sensor performance is attributed to the increased surface to volume ratio, reduction in dimensionality of the nanowire building blocks, formation of junctions between the initial nanowire and the secondary nanowires, and enhanced gas diffusion into the surfaces of the hierarchical nanostructures.

Keywords: metal oxide, nanostructure, hydrothermal, sensor

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Authors: Radhia Toujani, Jalel Akaichi

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In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

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Authors: M. Lesage, G. Raîche, M. Riopel, F. Fortin, D. Sebkhi

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This paper describes a Web server implementation of the hierarchical aggregate assessment process in the field of education. This process describes itself as a field of teamwork assessment where teams can have multiple levels of hierarchy and supervision. This process is applied everywhere and is part of the management, education, assessment and computer science fields. The E-Assessment website named “Cluster” records in its database the students, the course material, the teams and the hierarchical relationships between the students. For the present research, the hierarchical relationships are team member, team leader and group administrator appointments. The group administrators have the responsibility to supervise team leaders. The experimentation of the application has been performed by high school students in geology courses and Canadian army cadets for navigation patrols in teams. This research extends the work of Nance that uses a hierarchical aggregation process similar as the one implemented in the “Cluster” application.

Keywords: e-learning, e-assessment, teamwork assessment, hierarchical aggregate assessment

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Authors: Salima Kouici, Abdelkader Khelladi

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In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied.

Keywords: binary data, similarity measure, Tθ measures, agglomerative hierarchical clustering

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Authors: Carlos Alberto Ríos-Reyes, Efraín Casadiego-Quintero

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We propose a generalized workflow for mineralogy investigation of unconventional reservoirs using multi-scale imaging and pore-scale analyses. This workflow can be used for the integral evaluation of these resources. The Cretaceous La Luna Formation´s mudstones in the Middle Magdalena Valley Basin (Colombia) inherently show a heterogeneous pore system with organic and inorganic pores. For this reason, it is necessary to carry out the integration of high resolution 2D images of mapping by conventional petrography, scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy to describe their organic and inorganic porosity to understand the transport mechanism through pores. The analyzed rocks show several pore types, including interparticle pores, organoporosity, intraparticle pores, intraparticle pores, and microchannels and/or microfractures. The existence of interconnected pores in pore system of these rocks promotes effective pathways for primary gas migration and storage space for residual hydrocarbons in mudstones, which is very useful in this type of gas reservoirs. It is crucial to understand not only the porous system of these rocks and their mineralogy but also to project the gas flow in order to design the appropriate strategies for the stimulation of unconventional reservoirs. Keywords: mudstones; La Luna Formation; gas storage; migration; hydrocarbon.

Keywords: mudstones, La luna formation, gas storage, migration, hydrocarbon

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Authors: Mary Fargher

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This paper examines the opportunities of using a more hierarchical approach to geospatial enquiry in using GIS in school geography. A case is made that it is not just the lack of teacher technological knowledge that is stopping some teachers from using GIS in the classroom but that there is a gap in their understanding of how to link GIS use more specifically to the pedagogy of teaching geography with GIS. Using a hierarchical approach to geospatial enquiry as a theoretical framework, the analysis shows clearly how concepts of spatial distribution, interaction, relation, comparison, and temporal relationships can be used by teachers more explicitly to capitalise on the analytical power of GIS and to construct what can be interpreted as powerful geographical knowledge. An exemplar illustrating this approach on the topic of geo-hazards is then presented for critical analysis and discussion. Recommendations are then made for a model of progression for geography teacher education with GIS through hierarchical geospatial enquiry that takes into account beginner, intermediate, and more advanced users.

Keywords: digital geography, GIS, education, hierarchical geospatial enquiry, powerful geographical knowledge

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Authors: Ying Zhao, Xingyan Bin

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Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data.

Keywords: semi-supervised clustering, hierarchical agglomerative clustering, reference trees, distance constraints

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Authors: Sakiru Badmos, David R. Cole, Alberto Striolo

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It is known that confinement in nm-size pores affects many structural and transport properties of water and co-existing volatile species. Of particular interest for fluids in sub-surface systems, in catalysis, and in separations are reports that confinement can enhance the solubility of gases in water. Equilibrium molecular dynamics simulations were performed for aqueous H₂S confined in slit-shaped silica pores at 313K. The effect of pore width on the H₂S solubility in water was investigated. Other properties of interest include the molecular distribution of the various fluid molecules within the pores, the hydration structure for solvated H₂S molecules, and the dynamical properties of the confined fluids. The simulation results demonstrate that confinement reduces the H₂S solubility in water and that the solubility increases with pore size. Analysis of spatial distribution functions suggests that these results are due to perturbations on the coordination of water molecules around H₂S due to confinement. Confinement is found to dampen the dynamical properties of aqueous H₂S as well. Comparing the results obtained for aqueous H₂S to those reported elsewhere for aqueous CH₄, it can be concluded that H₂S permeates hydrated slit-shaped silica nano-pores faster than CH₄. In addition to contributing to better understanding the behavior of fluids in subsurface formations, these observations could also have important implications for developing new natural gas sweetening technologies.

Keywords: confinement, interfacial properties, molecular dynamic simulation, sub-surface formations

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Authors: W. J. Stępniowski, W. Florkiewicz, M. Norek, M. Michalska-Domańska, E. Kościuczyk, T. Czujko

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In this paper, two options of anodic alumina barrier layer thinning have been demonstrated. The approaches varied with the duration of the voltage step. It was found that too long step of the barrier layer thinning process leads to chemical etching of the nanopores on their top. At the bottoms pores are not fully opened what is disadvantageous for further applications in nanofabrication. On the other hand, while the duration of the voltage step is controlled by the current density (value of the current density cannot exceed 75% of the value recorded during previous voltage step) the pores are fully opened. However, pores at the bottom obtained with this procedure have smaller diameter, nevertheless this procedure provides electric contact between the bare aluminum (substrate) and electrolyte, what is suitable for template assisted electrodeposition, one of the most cost-efficient synthesis method in nanotechnology.

Keywords: anodic aluminum oxide, anodization, barrier layer thinning, nanopores

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Authors: Hubert Beisch, Janik Marx, Svenja Garlof, Roman Shvets, Ivan Grygorchak, Andriy Kityk, Bodo Fiedler

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Three-dimensional carbon materials can be used as electrode materials for energy storage systems such as batteries and supercapacitors. Fast charging and discharging times are realizable without reducing the performance due to aging processes. Furthermore high specific surface area (SSA) of three-dimensional carbon structures leads to high specific capacities. One newly developed carbon foam is Globugraphite. This interconnected globular carbon morphology with statistically distributed hierarchical pores is manufactured by a chemical vapor deposition (CVD) process from ceramic templates resulting from a sintering process. Via scanning electron (SEM) and transmission electron microscopy (TEM), the morphology is characterized. Moreover, the SSA was measured by the Brunauer–Emmett–Teller (BET) theory. Measurements of Globugraphite in an organic and inorganic electrolyte show high energy densities and power densities resulting from ion absorption by forming an electrochemical double layer. A comparison of the specific values is summarized in a Ragone diagram. Energy densities up to 48 Wh/kg and power densities to 833 W/kg could be achieved for an SSA from 376 m²/g to 859 m²/g. For organic electrolyte, a specific capacity of 100 F/g at a density of 20 mg/cm³ was achieved.

Keywords: BET, carbon foam, CVD process, electrochemical cell, Ragone diagram, SEM, TEM

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Eloise C. Tredenick, Troy W. Farrell, W. Alison Forster, Steven T. P. Psaltis

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The agriculture industry requires improved efficacy of sprays being applied to crops. More efficacious sprays provide many environmental and financial benefits. The plant leaf cuticle is known to be the main barrier to diffusion of agrochemicals within the leaf. The importance of a mathematical model to simulate uptake of agrochemicals in plant cuticles has been noted, as the results of each uptake experiments are specific to each formulation of active ingredient and plant species. In this work we develop a mathematical model and numerical simulation for the uptake of ionic agrochemicals through aqueous pores in plant cuticles. We propose a nonlinear porous diffusion model of ionic agrochemicals in isolated cuticles, which provides additions to a simple diffusion model through the incorporation of parameters capable of simulating plant species' variations, evaporation of surface droplet solutions and swelling of the aqueous pores with water. The model could feasibly be adapted to other ionic active ingredients diffusing through other plant species' cuticles. We validate our theoretical results against appropriate experimental data, discuss the key sensitivities in the model and relate theoretical predictions to appropriate physical mechanisms.

Keywords: aqueous pores, ionic active ingredient, mathematical model, plant cuticle, porous diffusion

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Authors: Fayssal Ynineb, Toufik Hadjersi, Fatsah Moulai, Wafa Achour

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Currently, tremendous attention has been paid to the rational design and synthesis of core/shell heterostructures for high-performance supercapacitors. In this study, the hierarchical NiO/ZnCo₂O₄ Core-Shell Nanorods Arrays were successfully deposited onto ITO substrate via a two-step hydrothermal and electrodeposition methods. The effect of the thin carbon layer between NiO and ZnCo₂O₄ in this multi-scale hierarchical structure was investigated. The selection of this structure was based on: (i) a high specific area of pseudo-capacitive NiO to maximize specific capacitance; (ii) an effective NiO-electrolyte interface to facilitate fast charging/discharging; and (iii) conducting carbon layer between ZnCo₂O₄ and NiO enhance the electric conductivity which reduces energy loss, and the corrosion protection of ZnCo₂O₄ in alkaline electrolyte. The obtained results indicate that hierarchical NiO/ZnCo₂O₄ present a high specific capacitance of 63 mF.cm⁻² at a current density of 0.05 mA.cm⁻² higher than that of pristine NiO and ZnCo₂O₄ of 6 and 3 mF.cm⁻², respectively. The carbon layer improves the electrical conductivity among NiO and ZnCo₂O₄ in the hierarchical NiO/C/ZnCo₂O₄ electrode. As well, the specific capacitance drastically increased to reach 125 mF.cm⁻². Moreover, this multi-scale hierarchical structure exhibits superior cycling stability with ~ 95.7 % capacitance retention after 65k cycles. These results indicate that the NiO/C/ZnCo₂O₄ nanocomposite material is an outstanding electrode material for supercapacitors.

Keywords: NiO/C/ZnCo₂O₄, specific capacitance, hydrothermal, supercapacitors

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Authors: Mohammad Alroaithi

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Porous polymeric materials have attracted a great attention due to their distinctive porous structure within a polymer matrix. They are characterised by the presence of external pores on the surface as well as inner interconnected windows. Conventional techniques to produce porous polymeric materials encounters major challenge in controlling the properties of the resultant structures including morphology, pores, cavities size, and porosity. Herein, we present a facile and versatile microfluidics technique for the fabrication of uniform porous polymeric structures with highly ordered and well-defined interconnected windows. The shapes of the porous structures can either be a microparticles or foam. Both shapes used microfluidics platform to first produce monodisperse emulsion. The uniform emulsions, were then consolidated into porous structures through UV photopolymerisation. The morphology, pores, cavities size, and porosity of the structures can be precisely manipulated by the flowrate. The proposed strategy might provide a key advantage for fabrication of uniform porous materials over many existing technologies.

Keywords: polymer, porous particles, microfluidics, porous structures

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Davood Mousanezhad, Ashkan Vaziri

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As a traditional cellular structure, hexagonal honeycombs are known for their high strength-to-weight ratio. Here, we introduce a class of fractal-appearing hierarchical metamaterials by replacing the vertices of the original non-hierarchical hexagonal grid with smaller hexagons and iterating this process to achieve higher levels of hierarchy. It has been recently shown that the isotropic in-plane Young's modulus of this hierarchical structure at small deformations becomes 25 times greater than its regular counterpart with the same mass. At large deformations, we find that hierarchy-dependent elastic buckling introduced at relatively early stages of deformation decreases the value of Poisson's ratio as the structure is compressed uniaxially leading to auxeticity (i.e., negative Poisson's ratio) in subsequent stages of deformation. We also show that the topological hierarchical architecture and instability-induced pattern transformations of the structure under compression can be effectively used to tune the propagation of elastic waves within the structure. We find that the hierarchy tends to shift the existing phononic bandgaps (defined as frequency ranges of strong wave attenuation) to lower frequencies while opening up new bandgaps. Deformation is also demonstrated as another mechanism for opening more bandgaps in hierarchical structures. The results provide new insights into the role of structural organization and hierarchy in regulating mechanical properties of materials at both the static and dynamic regimes.

Keywords: cellular structures, honeycombs, hierarchical structures, metamaterials, multifunctional structures, phononic crystals, auxetic structures

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Authors: Mackenzie Leake, Liyu Xia, Kamil Rocki, Wayne Imaino

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In the Hierarchical Temporal Memory (HTM) paradigm the effect of overlap between inputs on the activation of columns in the spatial pooler is studied. Numerical results suggest that similar inputs are represented by similar sets of columns and dissimilar inputs are represented by dissimilar sets of columns. It is shown that the spatial pooler produces these results under certain conditions for the connectivity and proximal thresholds. Following the discussion of the initialization of parameters for the thresholds, corresponding qualitative arguments about the learning dynamics of the spatial pooler are discussed.

Keywords: hierarchical temporal memory, HTM, learning algorithms, machine learning, spatial pooler

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Seung-Jun Yu, Yeong-Seop Ahn, Young-Min Ko, Hyoung-Kyu Song

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In order to achieve high data rate and increase the spectral efficiency, multiple input multiple output (MIMO) system has been proposed. However, multiple antennas are limited by size and cost. Therefore, recently developed cooperative diversity scheme, which profits the transmit diversity only with the existing hardware by constituting a virtual antenna array, can be a solution. However, most of the introduced cooperative techniques have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In this paper, we propose a cooperative cyclic delay diversity (CDD) scheme that uses hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication.

Keywords: MIMO, cooperative communication, CDD, hierarchical modulation

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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