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

Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN‐based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification‐based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR‐CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR‐CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR‐CFMs by a designed attention module to determine whether each PMR is a manipulated region and to localize the manipulated part at the pixel‐level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations.

Geometric rectification-based neural network architecture for image manipulation detection

Style-sensitive 3D model retrieval through sketch-based queries

In this paper, we present a novel approach to detect ground control points (GCPs) for stereo matching problem. First of all, we train a convolutional neural network (CNN) on a large stereo set, and compute the matching confidence of each pixel by using the trained CNN model. Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel. Finally, the selected GCPs are used to refine the matching costs, then we apply the new matching costs to perform optimization with semi-global matching algorithm for improving the final disparity maps. We evaluate our approach on the KITTI 2012 stereo benchmark dataset. Our experiments show that the proposed approach significantly improves the accuracy of disparity maps.

Detecting ground control points via convolutional neural network for stereo matching

In this paper, we first present the significance of information theory and several commonly referred concepts associated with it. Then, the communication channel models constructed by information theory are briefly introduced. Meanwhile, the channel capacity, as a key role in modeling a channel, is expatiated. In addition, source coding and channel coding are compared and explained with a number of simulations. By reading this paper, the readers are expected to understand the significance of information theory as well as the indispensable roles of source coding and channel coding in a communication system. Furthermore, most of the techniques and fundamentals introduced and analyzed in this paper are feasible for the big data analytics in cyber-physical systems, which pave the way for coding over these newborn systems.

Zhihan Lv

Papers

Geometric rectification-based neural network architecture for image manipulation detection

Style-sensitive 3D model retrieval through sketch-based queries

Detecting ground control points via convolutional neural network for stereo matching

Mathematical Models for Simulating Coded Digital Communication: A Comprehensive Tutorial by Big Data Analytics in Cyber-Physical Systems

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing