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

This paper presents a novel inner-outer lip facial animation
parameters (FAPs) interpolation within the framework of the MPEG-4
standard to allow for interpolation and realistic animation that can
be accomplished at very low bit rates. The proposed technique
applies Maximum a posteriori (MAP) estimation to interpolate the
inner lip FAPs given outer lip FAPs, or vice versa. The vectors of
the lip FAPs along with the first order and the second order
temporal derivatives over frames and the conditional probabilities
of these vectors are modeled as Gaussian distributions. As a result,
the MAP estimate of the inner lip FAPs can be obtained as a linear
transformation of the outer lip FAPs, or vice versa. When data
obtained from the training process are stored in the receiver, the
inner (or outer) lip FAPs can be interpolated from the outer (or
inner) lip FAPs, which are all that needs to be transmitted. Thus,
without any increase in the amount of transmitted information, a
more realistic talking animation that provides more visual information for speech reading can be achieved. Based on our
experiments, the proposed technique reduces the Percentage Normalized Mean Error (PNME) error by half over the standard
technique.

MPEG-4 outer-inner lip FAP interpolation

Due to the mechanics of the Atomic Force Microscope (AFM), there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. In this paper, an automated algorithm to flatten lines from AFM images is presented. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography. In addition a link between the flattening problem and MRI inhomogeneity (shading) is given and the proposed method is compared to an entropy based MRI inhomogeniety correction method.

/pdf/restoration-of-the-cantilever-bowing-distortion-in-atomic-16rhonec0t.pdf

Restoration of the cantilever bowing distortion in Atomic Force Microscopy images

regularizing parameter which trades fidelity to the data with smoothness of the restored image. For mostproblems of interest the choice of the regularizing parameter is not known a priori. Methods based on theproperties of the residuals and on the generalized cross-validation have been proposed for estimating the

Properties of different estimates of the regularizing parameter for the least-squares image restoration problem

Verfahren und vorrichtung zur verringerung von visuellem inhalt

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.

/pdf/deepcovid-fuse-a-multi-modality-deep-learning-model-fusing-3vtojmni.pdf

Aggelos K. Katsaggelos

Papers

MPEG-4 outer-inner lip FAP interpolation

Restoration of the cantilever bowing distortion in Atomic Force Microscopy images

Properties of different estimates of the regularizing parameter for the least-squares image restoration problem

Verfahren und vorrichtung zur verringerung von visuellem inhalt

DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels