Basic Principles of Unveiling Electromagnetic Problems Based on Deep Learning

2021 ◽  
pp. 23-41
Author(s):  
Qiang Ren ◽  
Yinpeng Wang ◽  
Yongzhong Li ◽  
Shutong Qi
Author(s):  
Dennis Craandijk ◽  
Floris Bex

In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics. More specifically, we propose an argumentation graph neural network (AGNN) that learns a message-passing algorithm to predict the likelihood of an argument being accepted. The experimental results demonstrate that the AGNN can almost perfectly predict the acceptability under different semantics and scales well for larger argumentation frameworks. Furthermore, analysing the behaviour of the message-passing algorithm shows that the AGNN learns to adhere to basic principles of argument semantics as identified in the literature, and can thus be trained to predict extensions under the different semantics – we show how the latter can be done for multi-extension semantics by using AGNNs to guide a basic search. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation.


2021 ◽  
Vol 8 ◽  
Author(s):  
Raffaele Nuzzi ◽  
Giacomo Boscia ◽  
Paola Marolo ◽  
Federico Ricardi

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.


2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Emma Marsdin ◽  
Seema Biswas

Medical schools responded to the first publication of Tomorrow’s Doctors with an abbreviated syllabus and a reduction in didactic teaching hours. Prescribing errors, however, have increased, and there is a perception amongst clinicians that junior doctors know less about the pathological basis of disease. We asked junior doctors how useful they thought their undergraduate teaching in pathology had been in their postgraduate training. We had 70 questionnaire responses from junior doctors within a single deanery and found that although almost every doctor, (96%), thought that pathology formed a major component of their postgraduate exams, most, (67%), thought that their undergraduate teaching left them unprepared for their postgraduate careers, and they had to learn basic principles, as they revised for postgraduate exams. Few used a pathology text for learning, most doctors, (91%), relying on question and answer revision resources for exam preparation. Perhaps, as revision materials are used so widely, they might be adapted for long-term deep learning, alongside clinical work. This presents an opportunity for pathologists, deaneries, royal colleges, and publishing houses to work together in the preparation of quality written and online material readily accessible to junior doctors in their workplace.


2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097630
Author(s):  
Bowen Teng ◽  
Hongjian Zhao

The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.


2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.


Author(s):  
Stellan Ohlsson
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document