EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201

Fast computation of the performance evaluation of biometric systems: application to multibiometric

The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos

Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Defining a Novel Meaning of the New Organic Architecture

Starting an overall investigation by categorizing current bio-inspired architectural design developments into “Material”, “Morphological”, and “Behavioral” to explore a novel definition of the “New Generation Organic Architecture”. At present, people are confronting the unprecedented unification of machine and biology which has been revealed by the means of advancing industrial processes towards the organic model. In his remarkable publication, “Out of Control: The New Biology of Machines, Social Systems, and the Economic World” (Kelly, 1995), Kevin Kelly makes an interesting observation that “Machines are becoming biological and the biological is becoming engineered”. In other words, the clear boundary of machine vs biology is blurring through current technological developments. In “Out of Control”, Kevin Kelly has further made several explicit points to support his views, that Industry will inevitably adopt bio-inspired methods: It takes less material to do the same job better. The complexity of built things now reaches biological complexity. Nature will not move, so it must be accommodated. The natural world itself—genes and life forms—can be engineered (and patented) just like industrial systems. All the crucial points described above can be easily observed in the architectural industry as well. Each statement corresponds with material optimization, multidisciplinary technologies, evolutionary processes, and genetic engineering which are all involved in current digital architectural design developments. After years of evolution, the developments of “Organic Architecture” have been now separated into various research focuses which are distant from the original idea coined by the well-known American architect, Frank Lloyd Wright. A group of followers still insist on maintaining Wright’s original idea to develop buildings which are green and sustainable, they fit or even blend into the surrounding environment as a whole. But since the power of personal computers and sophisticated modeling software has become relatively easy to access and is employed in all aspects of architectural design, various experiments have been conducted in the last decade, which try to outline a number of new definitions pertaining to “what are the essential ideas/principles of ‘Organic Architecture’?”. Nature has undoubtedly always been the greatest inspiration for the manmade industry, technology, and architecture. This development has only escalated with the assistance from computational technology over the last few decades. The thesis will preview the pros and cons of current design developments under the big umbrella of digital organic/bio-inspired architecture. This discussion will be categorized into three major divisions: “Morphological”, “Material”, and “Behavioral” owing to the different focus of computational applications within each one of them

형상 라이브러리를 이용한 응력발광 복합재료의 형태학적 특징 기반 미세구조 특성화 및 재구성

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 윤군진.이 논문에서는 응력 발광 세라믹 입자강화 복합재료에 대한 새로운 형상 특성에 의한 미세구조 특성화와 재구성이 진행되었다. 응력발광 세라믹 소재(Mechanoluminescent, ML)은 응력세기에 비례해서 빛을 방출한다. 이에 따라 응력발광 소재는 응력 센서로 활용하기 위한 연구가 진행되고 있다. 응력발광 소재가 빛 강도는 편향응력(deviatoric stress)에 비례한다는 점은 이미 알려져 있다. 응력발광 소재를 설계하는데 있어서, 입자가 받는 응력을 높일 수 있는 형상 특성을 찾는 것이 중요하다. 이런 선상에서 이 연구의 목적은 응력에 가장 민감하게 반응하는 형상 변수를 부피 평균한 폰 미제스 응력(volume averaged von Mises stress)과 형상 변수 사이의 통계적 상관관계 분석을 통해서 찾는 것이다. 응력발광 소재의 실제 형상을 확인하기 위해서 X선 마이크로컴퓨터 단층촬영으로 응력 발광 세라믹 복합재료의 단층 촬영해서 이미지를 확보하였다. 단층촬영 이미지 품질을 개선하고, 입자를 분리하기 위해서 가우시안 필터, 중앙값 필터, 워터셰드 알고리즘과 같은 이미지 처리 알고리즘을 사용하였다. 입자에 대한 13가지 형상 변수를 통해서 미세구조를 특성화했으며, 3차원 유한요소 해석을 통해서 각 입자에서 발생하는 부피 평균 폰 미제스 응력을 계산했다. 유한요소 해석 결과와 입자 형상 해석 결과를 연결해서 데이터베이스를 구축하고, 형상과 응력에 대한 상관관계를 조사했다. 이를 위해서 주성분 분석으로 형상변수와 부피평균응력의 상관행렬에서 독립된 성분을 찾았다. 통계적 해석을 통해서 입자 겉넓이와 shape index가 가장 부피평균응력에 민감하다는 것을 확인하였고, 이를 재구성 변수로 결정했다. 입자 분포는 최인접거리(nearest neighbor distance) 분포를 이용하였다. 이 연구에 도입된 아이디어로 실제 구성을 반영하기 위해서 복셀화된 9,687개 입자 단위 셀로 구성한 입자 형상 라이브러리를 사용했다. 목표 모델 변수와 재구성 모델 변수의 확률 분포를 일치하게 만드는 방법으로 성공적으로 재구성을 수행했다. 분포를 같게 만들기 위해서 최적화 기법인 담금질 기법(simulated annealing)을 사용했다. 재구성한 미세구조를 two point correlation function을 통해서 검증하였다. 그리고 재구성 모델의 데이터세트를 이용하여 형상 변수의 함수로 이루어진 새로운 TPCF 해석식을 도출하였다. 논문에서 제시한 알고리즘은 빠른 계산 속도에 장점이 있었으며, 입자 형상 라이브러리를 사용하여 실제 형상과 유사한 재구성 모델을 구성할 수 있다는 장점이 있다. 이 알고리즘을 통해서 생성한 수많은 미세구조 자료와 데이터 과학을 결합하여 응력발광 소재의 미세구조 설계에 활용을 기대할 수 있다.In this thesis, a new morphological feature-based microstructure characterization and reconstruction is developed for mechanoluminescent (ML) particulate composites. ML materials emit visible light proportional to the applied stresses. Therefore, ML materials have been studied for applications to stress sensor. It has also been known that the ML light intensity is proportional to the applied deviatoric stress in the particles. For the design of ML materials, it is critical to find out morphological features that can enhance the stress level within the particles. In the line of the research goal, morphological parameters that are most sensitive to stress enhancement were determined through statistical correlation analyses between a set of morphological parameters and volume-averaged von Mises stress (VAS) of ML particles. To use actual morphological shapes of ML particles, an X-ray micro-computed tomography (CT) is used. To improve the image quality and segment each particle, a series of image processing algorithms such as Gaussian filter, median filter, and watershed algorithm are applied. Microstructure characterization was conducted based on thirteen morphological variables. Three-dimensional finite element analyses were conducted to obtain the VAS for each particle. The database that consisted of particles morphological parameters and VAS was generated and used to find the correlation between morphology and VAS. To perform this, the principal component analysis (PCA) is adopted to find out spectral components of the correlation matrix between morphological parameters and VAS. As a result of statistical analysis, the surface area and the shape index were found to be the most sensitive morphological parameters to the VAS and used for reconstruction of microstructures. A local dispersion of ML particles was reconstructed by the nearest neighbor distance (NND). One of the novel approaches adopted in this thesis was using a particle shape library that consists of voxelized 9,687 particle unit cells. The reconstruction was successfully accomplished by matching their probabilistic distributions with those of target parameters. An optimization algorithm, simulated annealing (SA) was adopted for matching distributions. A two-point correlation function (TPCF) was used to verify the reconstructed microstructure. Also, the new analytical TPCF equation based on the morphological variables is generated with the reconstruction dataset. The algorithm proposed in this thesis has a salient advantage of computational efficiency and realistic microstructure reconstructed through the particle shape library. The combination of a dataset of reconstructed microstructure and novel data science is expected to be applied to the microstructure design of ML materials.Abstract I Table of Contents IV Table of Figures VI Chapter 1. Introduction 1 Chapter 2. Microstructure Characterization 5 2.1 Image Processing for Micro-Computed Tomography 5 2.2 Quantitative Morphological Variables of ML Particles 8 Chapter 3. Microstructure Sensitivity Analysis 11 3.1 Creation of Image-based FE Model 11 3.2 FE Mesh Sensitivity and Element Size Determination 13 3.3 3D Finite Element Analysis for VAS of Particles 15 3.4 Correlation between VAS and morphological variables 17 3.5. Design Parameters for Synthetic ML Particulate Composites 31 3.6 Principal Component Analysis 36 Chapter 4. Microstructure Reconstruction 43 4.1 Particle Sampling from Particle Shape Library 46 4.2 Particle Dispersion Matched Map 56 4.3 Particle Packing through Unit Cell Model 61 4.4 Verification with Two-Point Correlation Function 68 4.5 New Analytical Two-Points Correlation Function 73 Chapter 5. Conclusion 77 Bibliography 79 국문초록 82Maste

Worldwide Infrastructure for Neuroevolution: A Modular Library to Turn Any Evolutionary Domain into an Online Interactive Platform

Across many scientific disciplines, there has emerged an open opportunity to utilize the scale and reach of the Internet to collect scientific contributions from scientists and non-scientists alike. This process, called citizen science, has already shown great promise in the fields of biology and astronomy. Within the fields of artificial life (ALife) and evolutionary computation (EC) experiments in collaborative interactive evolution (CIE) have demonstrated the ability to collect thousands of experimental contributions from hundreds of users across the glob. However, such collaborative evolutionary systems can take nearly a year to build with a small team of researchers. This dissertation introduces a new developer framework enabling researchers to easily build fully persistent online collaborative experiments around almost any evolutionary domain, thereby reducing the time to create such systems to weeks for a single researcher. To add collaborative functionality to any potential domain, this framework, called Worldwide Infrastructure for Neuroevolution (WIN), exploits an important unifying principle among all evolutionary algorithms: regardless of the overall methods and parameters of the evolutionary experiment, every individual created has an explicit parent-child relationship, wherein one individual is considered the direct descendant of another. This principle alone is enough to capture and preserve the relationships and results for a wide variety of evolutionary experiments, while allowing multiple human users to meaningfully contribute. The WIN framework is first validated through two experimental domains, image evolution and a new two-dimensional virtual creature domain, Indirectly Encoded SodaRace (IESoR), that is shown to produce a visually diverse variety of ambulatory creatures. Finally, an Android application built with WIN, filters, allows users to interactively evolve custom image effects to apply to personalized photographs, thereby introducing the first CIE application available for any mobile device. Together, these collaborative experiments and new mobile application establish a comprehensive new platform for evolutionary computation that can change how researchers design and conduct citizen science online