A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

IMPRINTING POLYMERFILM ON PATTERNED SUBSTRATE

A method of applying a pattern on a topography includes first applying a polymer film to an elastormer member, such as PDMS, to form a pad. The pad is then applied to a substrate having a varying topography under pressure. The polymer film is transferred to the substrate due to the plastic deformation of the polymer film under pressure compared to the elastic deformation of the PDMS member pulls away from the polymer layer, thereby depositing the polymer layer, thereby depositing the polymer layer upon the substrate

An away day with a difference: developing new ways of working at the University of East London

This article describes how library staff from the University of East London planned and co-ordinated a successful staff development day which achieved its aim of broadening staff involvement and stimulating discussion about the library. As a result of the day concrete ideas emerged that have since been put into practice to the benefit of the service. The visits helped library staff see the service as one which in many regards matches the best in the sector

Emotion-corpus guided lexicons for sentiment analysis on Twitter.

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks