Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space

Studying the function of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the determination of the function of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the determination of protein functions in living cells. In this paper, we propose ProtNN, a novel approach for protein function prediction. Given an unannotated protein structure and a set of annotated proteins, ProtNN finds the nearest neighbor annotated structures based on protein-graph pairwise similarities. Given a query protein, ProtNN finds the nearest neighbor reference proteins based on a graph representation model and a pairwise similarity between vector embedding of both query and reference protein-graphs in structural and topological spaces. ProtNN assigns to the query protein the function with the highest number of votes across the set of k nearest neighbor reference proteins, where k is a user-defined parameter. Experimental evaluation demonstrates that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude of runtime compared to state-of-the-art approaches

Goods and services tax (GST) on construction capital cost and housing property price

Good and Service Tax (GST) an indirect broad-based conswnpti.on tax. Following with the implementation of GST in Malaysia on 1st April 2015, it is suspected that the construction capital cost and housing property price will increase accordingly. This study is aim to review the GST effect associated on construction capital cost and it influences towards housing developer and housing property price. Additionally, this study highlights what was the developer point of view on the GST given to them and also the housing price further proposes initiatives to the housing developers. Argument of GST effect is useful for the public administrators so that to re-consider the rate of GST and also beneficial to the construction parties to account with the GST implementation. As conclusion, this study review that GST do give an impact towards the construction capital cost, housing developer and housing property price in terms of knock-on effect

Effect of PAO-based γ-Fe2O3 and surfactant concentration on viscosity characteristic

This is a preliminary study on the viscosity characteristics of polyalphaolefin (PAO)- based γ-Fe2O3 under zero magnetic fields. By varying the concentration of magnetic nanoparticles (MNPs), PAO-based γ-Fe2O3 with different concentrations were synthesized by co-precipitation method. The effect of this factor on the viscosity characteristic of γ-Fe2O3 (< 30 nm) was investigated on the basic of a series of rheological measurement. The use of oleic acid (OA) as a coating agent and surfactant was also investigated by varying its concentration. The results show the concentration of MNPs and the amount of OA has obvious effect on viscosity characteristics of PAO-based γ-Fe2O3. In the case of comparison between the concentrations of MNPs, higher concentration of MNPs increased the viscosity of the PAO-based γ-Fe2O3 and exhibit nearly Newtonian behavior. The large amount of OA also exhibits the increment on viscosity characteristic of MNPs. The experimental data were compared with the Bingham and Casson model and the results revealed that the rheology of the polyalphaolefin (PAO)-based γ-Fe2O3 fitted the Casson model better

Goods and services tax (GST) on construction capital cost and housing property price

Good and Service Tax (GST) an indirect broad-based conswnpti.on tax. Following with the implementation of GST in Malaysia on 1st April 2015, it is suspected that the construction capital cost and housing property price will increase accordingly. This study is aim to review the GST effect associated on construction capital cost and it influences towards housing developer and housing property price. Additionally, this study highlights what was the developer point of view on the GST given to them and also the housing price further proposes initiatives to the housing developers. Argument of GST effect is useful for the public administrators so that to re-consider the rate of GST and also beneficial to the construction parties to account with the GST implementation. As conclusion, this study review that GST do give an impact towards the construction capital cost, housing developer and housing property price in terms of knock-on effect

DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP