Showing papers on "Field (mathematics) published in 2022"
21 Apr 2022
TL;DR: This book discusses methods in corpus linguistics: interpreting concordance lines, applications of corpora in applied linguistics, and more.
Abstract: Corpus Linguistics has revolutionised the world of language study and is an essential component of work in Applied Linguistics. This book, now in its second edition, provides a thorough introduction to all the key research issues in Corpus Linguistics, from the point of view of Applied Linguistics. The field has progressed a great deal since the first edition, so this edition has been completely rewritten to reflect these advances, whilst still maintaining the emphasis on hands-on corpus research of the first edition. It includes chapters on qualitative and quantitative research, applications in language teaching, discourse studies, and beyond. It also includes an extensive discussion of the place of Corpus Linguistics in linguistic theory, and provides numerous detailed examples of corpus studies throughout. Providing an accessible but thorough grounding to the fascinating, fast-moving field of Corpus Linguistics, this book is essential reading for the student and the researcher alike.
730 citations
TL;DR: In addition to the consequences derived from the effects of the virus, the mental health of people was impacted with high repercussions at the social and family level, as well as the teaching processes at the different levels of education where many students abandoned their studies.
Abstract: Current experiences at a global level are an inspiration for research in the academic field. There is much to learn, and society must carefully reflect on the moments lived in two years that for many have meant two centuries. Many difficulties remain to be resolved and a wide field to discover the unknown by medicine. With the difficulties that have arisen on a global scale because of COVID-19, health has been affected at the societal level; In addition to the consequences derived from the effects of the virus, the mental health of people was impacted with high repercussions at the social and family level, as well as the teaching processes at the different levels of education where many students abandoned their studies.
613 citations
TL;DR:
Abstract: The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems. Increasingly academic libraries are providing library users with systems, services, and collections created and delivered by machine learning. Academic libraries should adopt XAI as a tool set to verify and validate these resources, and advocate for public policy regarding XAI that serves libraries, the academy, and the public interest.
484 citations
01 Dec 2022
TL;DR: In this article , the authors provide an overview of various convolutional neural network (CNN) models and provide several rules of thumb for functions and hyperparameter selection, as well as open issues and promising directions for future work.
Abstract: A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work.
308 citations
TL;DR: The International Seminar on Urban Form (ISUF) as mentioned in this paper was founded by three schools of urban morphology, in England, Italy and France, following seminal work by two morphologists, M.R. Conzen and Saverio Muratori.
Abstract: The forces and events leading to the formation of the International Seminar on Urban Form (ISUF) are identified. ISUF is expanding the field of urban morphology beyond its original confines in geography, particularly into the domains of architecture and planning. Three schools of urban morphology, in England, Italy and France, are coming together, following seminal work by two morphologists, M.R.G. Conzen and Saverio Muratori. The bringing together of these schools provides the basis for an interdisciplinary field and the opportunity to establish common theoretical foundations for the growing number of urban morphologists in many parts of the world. ISUF's ambitious mission is to address real and timely issues concerning city building by providing a forum for thought and action which includes related disciplines and professions in different cultures. The potential of an interdisciplinary urban morphology to contribute to the understanding and management of urban development in a period of unprecedented change is discussed.
302 citations
TL;DR: This study surveyed the current progress of XAI and in particular its advances in healthcare applications, and introduced the solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios.
231 citations
TL;DR: A comprehensive review of deep facial expression recognition (FER) including datasets and algorithms that provide insights into these intrinsic problems can be found in this article , where the authors introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
Abstract: With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose, and identity bias. In this survey, we provide a comprehensive review of deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions for applicable implementations for each stage. For the state-of-the-art in deep FER, we introduce existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences and discuss their advantages and limitations. Competitive performances and experimental comparisons on widely used benchmarks are also summarized. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.
209 citations
17 Mar 2022
TL;DR: In this article , state-of-the-art experimental and numerical studies showing the most recent advancements in the field of rotary wing aerodynamics and aeroelasticity, with particular application to the rotorcraft and wind energy research fields are presented.
Abstract: This book contains state-of-the-art experimental and numerical studies showing the most recent advancements in the field of rotary wing aerodynamics and aeroelasticity, with particular application to the rotorcraft and wind energy research fields.
202 citations
TL;DR: Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made as mentioned in this paper , which is particularly true of the most popular deep neural network approaches currently in use.
177 citations
TL;DR: A comprehensive review of spatial gene expression technologies and data-analysis methods can be found in this article , along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used.
Abstract: The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .
175 citations
TL;DR: In this article , a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions is given, and the results show the differences and similarities among the YOLO versions and between CNNs.
TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Abstract: Abstract With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.
TL;DR: Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of transfer learning (TL) in knowledge transfer as mentioned in this paper .
TL;DR: Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of transfer learning (TL) in knowledge transfer.
TL;DR: In this paper , a far-field super-resolution Ghost Imaging (GI) technique was proposed that incorporates the physical model for GI image formation into a deep neural network, and the resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a farfield image with the resolution beyond the diffraction limit.
Abstract: Abstract Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.
TL;DR: In this article , a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.
TL;DR: In this paper, a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.
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TL;DR: The field of social physics has been a hot topic in the last few decades as mentioned in this paper , with many researchers venturing outside of their traditional domains of interest, but also taking from physics the methods that have proven so successful throughout the 19th and the 20th century.
TL;DR: Munich School of Management, Ludwig Maximilians University Munich, Munich, Germany Faculty of Economics and Business Administration, Babeș-Bolyai University, Cluj-Napoca, Romania Mitchell College of Business, University of South Alabama, Mobile, Alabama, USA Faculty of economics and management, Otto von Guerke University Magdeburg, Magdeburger, Germany Department of Economics, Business Economics, Aarhus University, AARhus, Denmark Hamburg University of Technology (TUHH), Hamburg, Germany as discussed by the authors
Abstract: Munich School of Management, Ludwig‐Maximilians‐University Munich, Munich, Germany Faculty of Economics and Business Administration, Babeș‐Bolyai University, Cluj‐Napoca, Romania Mitchell College of Business, University of South Alabama, Mobile, Alabama, USA Faculty of Economics and Management, Otto‐von‐Guericke‐University Magdeburg, Magdeburg, Germany Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark Hamburg University of Technology (TUHH), Hamburg, Germany
01 Jan 2023
TL;DR: In this paper , an intelligent deep learning model was proposed to identify the security threats and enable rapid detection and efficient treatment of polymorphic and encrypted viruses, such as Trojans, root kits, worms, and other malicious objects.
Abstract: When creating an antivirus, not only the latest security mechanisms are taken into account, but also the needs of users. That’s why this antivirus works together at high speed. The program interface allows it to choose the most optimal function. Free delivery allows a large number of users to rate this product. With the help of an intelligent deep learning model, a smart solution was proposed in this article to identify the security threats. It enables rapid detection and efficient treatment of polymorphic and encrypted viruses. Infected archives can now be detected when opened. This helps prevent its software from getting infected again. The program has the ability to create a confidence zone, which allows reducing the scanning time by creating a list of objects subject to scanning. This list should only contain sources that you are sure of. This application is designed to provide reliable protection for your computer. The proposed model allows it to protect its computer from all types of viruses and various Trojans, root kits, worms, and other malicious objects.
TL;DR: A survey of the last research on IoT and UAV technology applied in agriculture can be found in this paper , where the authors describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming.
Abstract: Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot technologies utilized in cultivation fields, which transform traditional farming practices into a new era of precision agriculture. In this paper, we perform a survey of the last research on IoT and UAV technology applied in agriculture. We describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming. Moreover, we present the role of UAV technology in smart agriculture, by analyzing the applications of UAVs in various scenarios, including irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. Furthermore, the utilization of UAV systems in complex agricultural environments is also analyzed. Our conclusion is that IoT and UAV are two of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.
TL;DR: In this article , the authors explore the place of certainty and uncertainty in therapeutic practice and suggest that there has been a theoretical shift in the field of family therapy from a first-to a second-order perspective.
Abstract: This paper explores the place of certainty and uncertainty in therapeutic practice. It suggests that for many, there has been a theoretical shift in the field of family therapy from a first- to a second-order perspective. To remain coherent with this shift in thinking requires a shift in practice in relation to this different way of thinking. The paper proposes one way of working towards this coherence through the use of a simple framework for working with uncertainty and highlights its application for a number of different contexts including training.
TL;DR: In this article , the authors review roadblocks to developing and assessing methods in computer analysis of medical images and provide recommendations on how to further address these problems in the future, and also discuss on-going efforts to counteract these problems.
Abstract: Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.
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28 Jul 2022
TL;DR: The Encyclopedia of Tourism Management and Marketing (ETM) as discussed by the authors is the most comprehensive reference work in the field of tourism management and marketing, curated by leading tourism scholar Dimitrios Buhalis.
Abstract: The Encyclopedia of Tourism Management and Marketing is, quite simply, the definitive reference work in the field. Carefully curated by leading tourism scholar Dimitrios Buhalis, this is the largest tourism management and marketing ontology that has ever been put together and offers a holistic examination of this interdisciplinary field This is a 4-volume set. Volume 1 contains entries A–D, Volume 2 contains entries E–I, Volume 3 contains entries J–R and Volume 4 contains entries S–Z. Page numbers start from 1 in each volume.
TL;DR: In this paper , the material choice and device design for organic field effect transistor (FOFET) devices and circuits, as well as the demonstrated applications are summarized in detail, and the technical challenges and potential applications of FOFETs in the future are discussed.
Abstract: Abstract Flexible electronics have suggested tremendous potential to shape human lives for more convenience and pleasure. Strenuous efforts have been devoted to developing flexible organic field-effect transistor (FOFET) technologies for rollable displays, bendable smart cards, flexible sensors and artificial skins. However, these applications are still in a nascent stage for lack of standard high-performance material stacks as well as mature manufacturing technologies. In this review, the material choice and device design for FOFET devices and circuits, as well as the demonstrated applications are summarized in detail. Moreover, the technical challenges and potential applications of FOFETs in the future are discussed.
TL;DR: In this paper , a far-field super-resolution Ghost Imaging (GI) technique was proposed that incorporates the physical model for GI image formation into a deep neural network, and the resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a farfield image with the resolution beyond the diffraction limit.
Abstract: Abstract Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.
TL;DR: In this article , a survey of domain adaptation methods for medical image analysis is presented, and the authors categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods.
Abstract: Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges and future directions of this energetic research field.
TL;DR: The phase-field method as mentioned in this paper is a density-based computational method at the mesoscale for modeling and predicting the temporal microstructure and property evolution during materials processes, which can provide guidance to designing materials for optimum properties or discovering novel mesoscales phenomena or new materials functionalities.
TL;DR: In this paper , the authors conduct a systematical survey of knowledge graph-based recommender systems and propose several potential research directions in this field, focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.
Abstract: To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.