There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Image Super-Resolution Using Deep Convolutional Networks

We present a regularized mixed norm multichannel im- age restoration algorithm. The problem of multichannel restoration using both within- and between-channel deterministic information is considered. For each channel a functional that combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters just mentioned are adjusted based on the partially restored image. © 2005 SPIE and IS&T.

Iterative regularized mixed norm multichannel image restoration

Starting in the 1890s the artist Paul Gauguin (1848-1903) created a series of prints and transfer drawings using techniques that are not entirely understood. To better understand the artist's production methods, photometric stereo was used to assess the surface shape of a number of these graphic works that are now in the collection of the Art Institute of Chicago. Photometric stereo uses multiple images of Gauguin's graphic works captured from a fixed camera position, lit from multiple specific angles to create an interactive composite image that reveals textural characteristics. These active images reveal details of sequential media application upon experimental printing matrices that help resolve longstanding art historical questions about the evolution of Gauguin's printing techniques. Our study promotes the use of photometric stereo to capitalize on the increasing popularity of Reflectance Transformation Imaging (RTI) among conservators in the world's leading museums.

Surface shape studies of the art of Paul Gauguin

for the estimation of the sparse component of an outliercorrupted low-rank matrix, when linearly transformed composite data are observed. The model constitutes a generalization of robust principal component analysis. The problem considered herein is applicable in various practical scenarios, such as foreground detection in blurred and noisy video sequences and detection of network anomalies among others. The proposed algorithm models the low-rank matrix and the sparse component using a hierarchical Bayesian framework, and employs a variational approach for inference of the unknowns. The effectiveness of the proposed algorithm is demonstrated using real life experiments, and its performance improvement over regularization based approaches is shown.

/pdf/a-variational-approach-for-sparse-component-estimation-and-vwuu2d9axx.pdf

A Variational Approach for Sparse Component Estimation and Low-Rank

We consider DNA implementations of databases for digital signals with retrieval and mining capabilities. Digital signals are encoded in DNA sequences and retrieved through annealing between query DNA primers and data carrying DNA target sequences. The hybridization between query and target can be non-specific containing multiple mismatches thus implementing similarity-based searches. In this paper we examine theoretically and by simulation the efficiency of such a system by estimating the concentrations of query-target duplex formations at equilibrium. A coupled kinetic model is used to estimate the concentrations. We offer a derivation that results in an equation that is guaranteed to have a solution and can be easily and accurately solved computationally with bi-section root-finding methods. Finally, we also provide an approximate solution at dilute query concentrations that results in a closed form expression. This expression is used to improve the speed of the bi-section algorithm and also to find a closed form expression for the specificity ratios.

Aggelos K. Katsaggelos

Papers

Iterative regularized mixed norm multichannel image restoration

Surface shape studies of the art of Paul Gauguin

A Variational Approach for Sparse Component Estimation and Low-Rank

In silico estimation of annealing specificity of query searches in DNA databases

Vlsi implementation of an iterative image restoration algorithm.