Data Mining - Concepts and Techniques.

Statistics for Spatial Data.

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

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This paper addresses the problem of discovering temporal and contextual relationships across document, data, and software categories of electronic records. We designed a methodology to discover unknown relationships by conducting file system and file content analyses. The work also investigates automation of metadata extraction from engineering drawings and storage requirements for metadata extraction. The methodology has been applied to extracting information from a test collection of electronic records about the NAVY ship (TWR 841) archived by the US National Archive (NARA). This test collection represents a problem of unknown relationships among files that include 784 2D image drawings and 22 CAD models.

/pdf/a-methodology-for-file-relationship-discovery-13pcfeci8j.pdf

A Methodology for File Relationship Discovery

This work presents a web-based interactive neural network (NN) calculator and a NN inefficiency measurement that has been investigated for the purpose of detecting trojans embedded in NN models. This NN Calculator is designed on top of TensorFlow Playground with in-memory storage of data and NN coefficients. Its been extended with additional analytical, visualization, and output operations performed on training datasets and NN architectures. The analytical capabilities include a novel measurement of NN inefficiency using modified Kullback-Liebler (KL) divergence applied to histograms of NN model states, as well as a quantification of the sensitivity to variables related to data and NNs. Both NN Calculator and KL divergence are used to devise a trojan detector approach for a variety of trojan embeddings. Experimental results document desirable properties of the KL divergence measurement with respect to NN architectures and dataset perturbations, as well as inferences about embedded trojans.

Neural Network Calculator for Designing Trojan Detectors.

While WIPP has been designed for big image data experiments, its execution speed depends on the underlying hardware, which can vary with each deployment. Readers may be interested in optimizing WIPP for their hardware or in rewriting existing algorithms to better utilize available RAM, CPU, and bandwidth. In this chapter, our goal is to assist the reader in:


Choosing hardware for WIPP deployment


Characterizing image data to estimate storage and processing requirements


Measuring execution time


Leveraging several known models for parallel execution of image processing algorithms

Interoperability Between Software and Hardware

One of the main challenges for many scientific communities is reproducibility of published discoveries [1]. In the current publication process, manuscripts contain only data summaries and models. The trend in many state-of-the-art publication venues is to include references in the manuscripts that point to online accessible software and raw data [2]. However, this trend does not address the problems of reproducibility and traceability of published summaries and models to the individual contributing data points for analyses from microscopy images reaching Gigapixel and Terabyte sizes. The reasons lie in the extra expertise and infrastructure needed to share Gigapixel images and intermediate image features for visual validation. In addition, summarization and modelling steps during a discovery include userdriven selections of data points and statistical models. There is a need to design discovery-assisting and summary validation software systems over very large images that would advance discoveries and improve reproducibility of published scientific results.

Interactive Web-based Spatio-Statistical Image Modeling from Gigapixel Images to Improve Discovery and Traceability of Published Statistical Models

Microscopy image-based measurement variability in high-throughput imaging experiments for biological drug discoveries, such as COVID-19 therapies was addressed in this study. Variability of measurements came from (1) computational approaches (methods), (2) implementations of methods, (3) parameter settings, (4) chaining methods into workflows, and (5) stabilities of floating-point arithmetic on diverse hardware. Measurement variability was addressed by (a) introducing interoperability between algorithms, (b) enforcing automated capture of computational provenance and parameter settings, and (c) quantifying multiple sources of variabilities for 10 nucleus measurements, from 8 workflow streams, executed in 2 workflow graph configurations, on 2 computational hardware platforms at 2 locations. Using modified Mean Absolute Error (mMAE [%]) to compare measurements, We concluded that for the task of image-based nucleus measurements the variability sources were (1) implementations (0.10 % - 5.72 % per measurement), (2) methods (3.08 % - 3.11 % between Otsu thresholding and CellPose segmentation), (3) parameters (1.16 %-1.17 % between 4- and 8-neighbor connectivity), (4) workflow graph construction and computer hardware (negligible).

/pdf/quantifying-variability-in-microscopy-image-analyses-for-4s2ps1ucu4.pdf

Peter Bajcsy

Papers

A Methodology for File Relationship Discovery

Neural Network Calculator for Designing Trojan Detectors.

Interoperability Between Software and Hardware

Interactive Web-based Spatio-Statistical Image Modeling from Gigapixel Images to Improve Discovery and Traceability of Published Statistical Models

Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery