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The applications of machine learning in HIV neutralizing antibodies research-A systematic review

ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 134卷 102429-102429页

作者： Danaila, Vlad-Rares Avram, Speranta Buiu, Catalin Univ Politehn Bucuresti Dept Automatic Control & Syst Engn 313 Splaiul Independentei Bucharest 060042 Romania Univ Bucharest Fac Biol Dept Anat Anim Physiol & Biophys 91 95 Splaiul Independentei Bucharest 050095 Romania

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody- virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

关键词： HIV antibody Neutralization potency Neutralization breadth Epitope detection Machine learning Deep learning Neural network Feature selection Unsupervised learning Clustering generative algorithms Data preprocessing

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Using Hybrid Discriminative-generative Models for Binary Classification

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2019年第4期53卷 320-327页

作者： Abroyan, N. Natl Polytech Univ Armenia Inst Informat & Telecommun Technol & Elect Yerevan 375009 Armenia

Discriminative and generative machine learning algorithms have been successfully used in different classification tasks during the last several decades. They both have some advantages and disadvantages and depending on a problem, one type of algorithm performs better than the other one. In this paper we contribute to the research of combination of both approaches and propose literature based a hybrid discriminative-generative generic model. Also, we propose hybrid model structure finding and building a new algorithm. We present theoretical and practical advantages of the hybrid model over its consisting algorithms, efficiency of the model structure finding algorithm, then perform experiments and compare results.

关键词： machine learning classification discriminative algorithms generative algorithms hybrid model

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Latent History 19

Latent History

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27th ACM International Conference on Multimedia (MM)

作者： Anadol, Refik Refik Anadol Studio Los Angeles CA 90026 USA

ISBN: (纸本)9781450368896

Latent History is a time and space exploration into Stockholm's past and ultimately present, through the deployment of machine learning algorithms trained on datasets from both archival and contemporary photographs. Through the exploration of photographic memories from the past 150 years, this exhibition aims to investigate and re-imagine collective memory, hidden layers of history, and the consciousness of a city that otherwise might remain unseen.

关键词： Machine learning archival data generative algorithms data visualization

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WOODEN TRUSSES RECONSTRUCTION AND ANALYSIS THROUGH PARAMETRIC 3D MODELING 8

WOODEN TRUSSES RECONSTRUCTION AND ANALYSIS THROUGH PARAMETRI...

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8th International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures (3D-ARCH)

作者： Prati, D. Zuppella, G. Mochi, G. Guardigli, L. Gulli, R. Univ Bologna DA Bologna Italy

This paper aims to indicate a new methodological approach, based on generative algorithms, to attempt a more in-depth and transversal understanding of the behavior of these wooden structures. The developed method accelerates modeling procedures and brings on new tools for analyzing these structural systems when surveyed through TLS devices. The main topic of this work is the most recent step of a wider research project that has analyzed a few wooden roofing structures in the area of bologna. These case studies are represented by a set of important churches in Bologna, all built between the 16th and 18th centuries, whose pitched roofs are supported by timber trusses. Among them, the most impressive is the wooden truss in the St. Peter Cathedral that has approximately 26 meters of span, 7 meters of height for nearly 9 tons of weight. It also shows a complex static conception with the coexistence of an external nondeformable triangle and an internal virtual discharging arc. The focus is on the transformation of the point cloud into 3D models using parametric modeling tools such as Grasshopper generative algorithms. These algorithms, once created for a single truss, allow to automatically generating 3D models of all trusses, changing only input parameters.

关键词： Building heritage Terrestrial Laser Scanning generative algorithms Structural systems reverse engineering Cloud to 3D model comparison

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Anarchy A Library for Incremental Chaos 19

Anarchy A Library for Incremental Chaos

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14th International Conference on the Foundations of Digital Games (FDG)

作者： Mawhorter, Peter Wellesley Coll Wellesley MA 02181 USA

ISBN: (纸本)9781450372176

Pseudo-random number generators are ubiquitous components of content generation systems, because their outputs are difficult to predict but also repeatable given an initial seed. These properties make them especially useful as the basis for "random" decisions during a generative process, as they allow the process to be chaotic but also repeatable. This paper describes an open-source family of pseudo-random algorithms which allow for shuffling and distributing items in a reversible and incremental manner. To demonstrate the applicability of these algorithms, I show how they have been used in the creation of a word-search game which includes strong guarantees about the distribution of words that can be discovered.

关键词： generative algorithms Computational Creativity Pseudo-random Number Generation Noise Functions Chaos Procedural Content Generation

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algorithms, Oppression, and Mental Illness on Social Media

Algorithms, Oppression, and Mental Illness on Social Media

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Feuston, Jessica L. Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9781450359719

My research explores how individuals with mental illness express themselves online and off. Through digital ethnography, including interviews with Instagram users and manual collection of public content on Instagram, I have holistically examined the experience of mental illness as expressed through social media. This user-centric approach reveals and addresses the limitations of computational techniques, which my dissertation work will address by combining qualitative methods with generative algorithms to explore new 'ways of seeing' mental illness. I will create new tools enabling users to generate representations from their own posts in support of creating new representations of mental illness, advancing algorithmic fairness, and confronting technological forms of oppression online.

关键词： Mental illness social media digital ethnography generative algorithms anti-oppressive design

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An integrated framework for multi-criteria optimization of thin concrete shells at early design stages

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ADVANCED ENGINEERING INFORMATICS 2018年 38卷 330-342页

作者： Gomes, Carlos Parente, Manuel Azenha, Miguel Lino, Jose Carlos Shay Murtagh Precast Ltd Mullingar N91 WY91 Westmeath Ireland INESC TEC Inst Syst & Comp Engn Technol & Sci Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Minho ISISE Sch Engn Dept Civil Engn Campus Azurem P-4800058 Guimaraes Portugal

Thin shells are crucially dependent on their shape in order to obtain proper structural performance. In this context, the optimal shape will guarantee performance and safety requirements, while minimizing the use of materials, as well as construction/maintenance costs. Thin shell design is a team-based, multidisciplinary, and iterative process, which requires a high level of interaction between the various parties involved, especially between the Architecture and Engineering teams. As a result of technological development, novel concepts and tools become available to support this process. On the one hand, concepts like Integrated Project Delivery (IPD) show the potential to have a high impact on multidisciplinary environments such as the one in question, supporting the early decision-making process with the availability of as much information as possible. On the other hand, optimization techniques and tools should be highlighted, as they fit the needs and requirements of both the shell shape definition process and the IPD concept. These can be used not only to support advanced design stages, but also to facilitate the initial formulation of shape during the early interactions between architect and structural engineer from an IPD point of view. This paper proposes a methodology aimed at enhancing the interactive and iterative process associated with the early stages of thin shell design, supported by an integrated framework. The latter is based on several tools, namely Rhinoceros 3D, Grasshopper, and Robot Structural Analysis. In order to achieve full integration of the support tools, a custom devised module was developed, so as to allow interoperability between Grasshopper and Robot Structural Analysis. The system resorts to various technologies targeted at improving the shell shape definition process, such as formfinding techniques, parametric and generative models, as well as shape optimization techniques that leverage on multi criteria evolutionary algorithms. Th

关键词： Thin concrete shells Interoperability Optimization generative algorithms

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A Novel Algorithm for Neural Network Architecture Generation, Parameter Optimization and Feature Selection - ParFeatArch Generator 18

A Novel Algorithm for Neural Network Architecture Generation...

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2nd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (ISMSI)

作者： Brito, Ricardo Fong, Simon Wu, Yaoyang Deb, Suash Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China Victoria Univ Decis Sci & Modeling Program Melbourne Vic 8001 Australia

ISBN: (纸本)9781450364126

In this paper, we propose ParFeatArch Generator, a new algorithm for generating Neural Network architectures with optimal features and parameters through Particle Swarm Optimization. Selecting the best architecture for a Neural Network is usually done through a trial and error process, in which the number of layers is selected usually based on previous experience and then the network is trained and tested. When using Neural Networks as classifiers in feature selection algorithms, usually the number of layers in the Neural Network is selected prior to using the Neural Network as a classifier to the feature selection algorithm. In this work we propose a new generative algorithm called ParFeatArch Generator, which is based on PSO and combines the feature selection process with the Neural Network architecture selection process and parameter optimization in one algorithm which generates the Neural Network topology with optimal parameters while at the same time performs feature selection and evaluates the Neural Network topology to determine its quality. With the proposed algorithm, given a dataset, it is possible to end up with the optimal features on the dataset and with an optimal Neural Network classifier with optimal parameters for such features.

关键词： Particle Swarm Optimization (PSO) Neural Networks Feature Selection generative algorithms

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A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System

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SPORTS MEDICINE-OPEN 2018年第1期4卷 24-24页

作者： Colyer, Steffi L. Evans, Murray Cosker, Darren P. Salo, Aki I. T. Univ Bath CAMERA Bath BA2 7AY Avon England Univ Bath Dept Hlth Bath BA2 7AY Avon England Univ Bath Dept Comp Sci Bath BA2 7AY Avon England

Background: The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. Main body: This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the Accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not ail reiltly in widespread use within biomechanics. Conclusions: This review aims to introduce the key state of the art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

关键词： Automatic analysis Body model Cameras Discriminative approaches Gait generative algorithms Motion capture Rehabilitation Sports biomechanics Technique

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Generating Neural Networks with Optimal Features through Particle Swarm Optimization 2017

Generating Neural Networks with Optimal Features through Par...

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International Conference on Big Data and Internet of Things (BDIOT)

作者： Brito, Ricardo Fong, Simon Zhuang, Yan Wu, Yaoyang Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China

ISBN: (纸本)9781450354301

In this paper, we propose a new algorithm for generating Neural Network architectures with optimal features through Particle Swarm Optimization. Selecting the best architecture for a Neural Network is usually done through a trial and error process, in which the number of layers is selected usually based on previous experience and then the network is trained and tested. When using Neural Networks as classifiers in feature selection algorithms, usually the number of layers in the Neural Network is selected prior to using the Neural Network as a classifier to the feature selection algorithm. In this paper we propose a new generative algorithm based on PSO which combines the feature selection process with the Neural Network topology selection process in one algorithm which generates the Neural Network topology while at the same time performs feature selection and evaluates the Neural Network topology to determine its quality. With the proposed algorithm, given a dataset, it is possible to end up with the optimal features on the dataset and with an optimal Neural Network classifier for such features.

关键词： Particle Swarm Optimization (PSO) Neural Networks Feature Selection generative algorithms

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