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Self-Reproduction and Evolution in Cellular Automata: 25 Years after Evoloops

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arXiv 2024年

作者： Sayama, Hiroki Nehaniv, Chrystopher L. Binghamton Center of Complex Systems Binghamton University State University of New York BinghamtonNY United States Waseda Innovation Lab Waseda University Tokyo Japan Waterloo Institute for Complexity and Innovation Departments of Systems Design Engineering of Electrical & Computer Engineering and of Applied Mathematics University of Waterloo WaterlooON Canada

The year of 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton’s self-reproducing loops which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artificial Life research has since undergone several important developments. Although it experienced a relative dormancy of activities for a while, the recent rise of interest in open-ended evolution and the success of continuous cellular automata models have brought researchers’ attention back to how to make spatio-temporal patterns self-reproduce and evolve within spatially distributed computational media. This article provides a review of the relevant literature on this topic over the past 25 years and highlights the major accomplishments made so far, the challenges being faced, and promising future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Cellular automata

COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images

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arXiv 2022年

作者： Gunraj, Hayden Tuinstra, Tia Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

Computed tomography (CT) has been widely explored as a COVID-19 screening and assessment tool to complement RT-PCR testing. To assist radiologists with CT-based COVID-19 screening, a number of computer-aided systems have been proposed. However, many proposed systems are built using CT data which is limited in both quantity and diversity. Motivated to support efforts in the development of machine learning-driven screening systems, we introduce COVIDx CT-3, a large-scale multinational benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries, which to the best of our knowledge represents the largest, most diverse dataset of COVID-19 CT images in open-access form. Additionally, we examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding that significant geographic and class imbalances remain despite efforts to curate data from a wide variety of sources. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs

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arXiv 2023年

作者： Tweel, James E.D. Ecclestone, Benjamin R. Boktor, Marian Simmons, James Alexander Tummon Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada IllumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada Vision and Image Processing Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry and structure, meaning an individual tissue section must be prepared for each desired staining contrast. This not only consumes valuable tissue samples but also introduces delays in essential diagnostic timelines. In this work, virtual histochemical staining is developed using label-free photon absorption remote sensing (PARS) microscopy. We present a method that generates virtually stained histology images that are indistinguishable from the gold standard hematoxylin and eosin (H&E) staining. First, PARS label-free ultraviolet absorption images are captured directly within unstained tissue specimens. The radiative and non-radiative absorption images are then preprocessed, and virtually stained through the presented pathway. The preprocessing pipeline features a self-supervised Noise2Void denoising convolutional neural network (CNN) as well as a novel algorithm for pixel-level mechanical scanning error correction. These developments significantly enhance the recovery of sub-micron tissue structures, such as nucleoli location and chromatin distribution. Finally, we used a cycle-consistent generative adversarial network CycleGAN architecture to virtually stain the preprocessed PARS data. Virtual staining is applied to thin unstained sections of malignant human skin and breast tissue samples. Clinically relevant details are revealed, with comparable contrast and quality to gold standard H&E-stained images. This work represents a crucial step to deploying label-free microscopy as an alternative to standard histopathology techniques. © 2023, CC BY.

关键词： Fluorescence microscopy

Cross-day analysis of Multicode Surface Electromyography based Biometrics for Personal Identification

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Cross-day analysis of Multicode Surface Electromyography bas...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ashirbad Pradhan Jiayuan He Ning Jiang Department of Systems Design Engineering Engineering Bionics Lab Faculty of Engineering University of Waterloo Waterloo Canada National Clinical Research Center for Geriatrics West China Hospital Sichuan University China Manufacturing Sichuan University Chengdu China

Recently, surface electromyography (sEMG) has emerged as a novel biometric trait for personal identification, potentially providing a superior spoof-resistant solution over existing traits. The sEMG possesses a unique dual-mode security: they differ between individuals (biometric-mode), and different gestures have different sEMG characteristics (knowledge-mode). To leverage the knowledge-mode facet of the dual-mode security, the previous studies have utilized a multicode framework involving the fusion of codes (gestures), however, the analysis involved data recorded on a single day and from a small subject-pool. In this study, wrist EMG data collected from 43 participants over three different days while performing static hand/wrist gestures was utilized in two cross-day analyses, where the training and testing data were from different days. Three levels of fusion, score, rank, and decision were investigated to determine the optimal fusion scheme. The results showed that the score-level fusion scheme resulted in a median rank-1 accuracy of 77.9% and rank-5 accuracy of 99.6%, all significantly higher (p<0.001) than the respective single-code gesture. Our results showed that the multicode sEMG biometric framework provides superior identification performance in a more realistic cross-day scenario.

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Vehicle Group Identification and Evolutionary Analysis Using Vehicle Trajectory Data

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SSRN

SSRN 2023年

作者： Lei, Cailin Shangguan, Qiangqiang Ji, Yuxiong Du, Yuchuan Samuel, Siby Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Department of Civil and Environmental Engineering University of Waterloo WaterlooONN2L 3G1 Canada User Performance Lab Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Vehicles often move forward in groups on the highways, especially when speed and density are high simultaneously. Abnormal maneuvers of a vehicle in a group influence multiple vehicles surrounding it, potentially leading to traffic accidents. We propose an approach to identify vehicle groups and analyse the factors influencing their evolutions using vehicle trajectory data. The proposed approach quantifies the interactions between neighboring vehicles based on the potential energy field, represents the interactive relationships among multiple vehicles using a multi-vehicle interaction network, and adopts the process of sub-network segmentation to identify vehicle groups. A random-parameter logistic regression (RPLG) model is developed to examine the influence of vehicle group features on vehicle group split. The effectiveness of the proposed approach is demonstrated in a case study using a real-world dataset. The case study reveals that: (1) the interaction strengths between vehicles tend to increase with increasing speed, (2) the interaction strength between a vehicle and its preceding vehicle is the largest, while the interaction strengths between a vehicle and its vehicles on its sides are the lowest, and (3) higher longitudinal and lateral speeds, larger fluctuations in longitudinal speeds and accelerations, larger group size, larger distances between vehicles, more lanes occupied by a vehicle group, and higher vehicle interactions significantly increase the probability of vehicle group split. The findings of this study can potentially support the traffic management and development of autonomous driving technology in connected vehicle environments. © 2023, The Authors. All rights reserved.

关键词： Vehicles

Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction

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Towards an Interaction Architecture for the iCub Robot: Soci...

IEEE-RAS International Conference on Humanoid Robots

作者： Sahand Shaghaghi Pourya Aliasghari Bryan Tripp Britt Anderson Kerstin Dautenhahn Chrystopher L. Nehaniv Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Psychology University of Waterloo 200 University Ave. W. Waterloo Ontario Canada

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Gaze behaviour plays a crucial role in social interactions extending to Human-Robot Interactions. Humanoid robots equipped with anthropomorphic vision systems may be expected to employ natural gaze behaviour in interactions with humans. Here, we implemented a novel first-person human gaze state detection tool on the iCub robot and assessed its gaze detection accuracy. Using this tool, we then developed two social gaze interaction architectures for the iCub robot. The Social Gaze Space theory (SGS) of Jording and colleagues was the theoretical framework for building our first architecture, SGS Base (SGS-B). Our second architecture, SGS Interaction Architecture (SGS-IA), is the extended version of SGS Base that lets the robot take on different higher-level goals in an interaction, including being attentive to the interaction partner or attempting to direct their attention to an object. These architectures support dyadic social interactions involving initiating and responding to joint attention towards objects of interest, and are expected to allow the robot to interact in a more human-like manner in social interactions. The gaze detection accuracy of the iCub robot equipped with our choice of algorithms exceeded that of previously implemented methods. System validation trials confirmed that the robot detects the social gaze states correctly in the majority of the instances, allowing for behavioural control according to the Social Gaze Space theory. Validation tests presented in this article demonstrate the functionality of the SGS Interaction Architecture and also highlight the differences in personal interaction dynamics when the humanoid robot has no particular goals as opposed to when it has interactional goals appropriate to a teaching scenario.

关键词： Accuracy Architecture Education Humanoid robots Human-robot interaction Modulation Aerospace electronics Visual systems System validation Standards

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

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arXiv 2022年

作者： Pavlova, Maya Tuinstra, Tia Aboutalebi, Hossein Zhao, Andy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Cheriton School of Computer Science University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Text Detection & Recognition in the Wild for Robot Localization

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arXiv 2022年

作者： Raisi, Zobeir Zelek, John Vision and Image Processing Lab Systems Design Engineering Department University of Waterloo WaterlooONN2L 3G1 Canada

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due such factors as pose, irregular text, illumination and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture’s performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark. © 2022, CC BY-NC-SA.

关键词： Robot applications

Performance optimization of surface electromyography (sEMG) based biometric sensing system for both verification and identification

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arXiv 2021年

作者： Pradhan, Ashirbad He, Jiayuan Jiang, Ning Engineering Bionics Lab Department of Systems Design Engineering Faculty of Engineering University of Waterloo Waterloo Canada

Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. Since EMG system parameters, such as the feature extraction methods and the number of channels, have been known to affect system performances, it is important to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters. In this study, three robust feature extraction methods, Time-domain (TD) feature, Frequency Division Technique (FDT), and Autoregressive (AR) feature, and their combinations were investigated while the number of channels varying from one to eight. For these system parameters, the performance of sixteen static wrist and hand gestures was systematically investigated in two authentication modes: verification and identification. The results from 24 participants showed that the TD features significantly (p © 2021, CC BY-NC-SA.

关键词： Biometrics