Neural networks in automated measurement systems: state of the art and new research trends

2002 ◽  
Author(s):  
O. Postolache ◽  
P. Girao ◽  
M. Pereira
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Research Trends ◽  
Automated Measurement ◽  
Measurement Systems ◽  
New Research

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

Autonomous Flight

2018 ◽  
Vol 1 (1) ◽  
pp. 29-52 ◽  
Author(s):  
Sarah Tang ◽  
Vijay Kumar
Keyword(s):  
High Speed ◽  
State Of The Art ◽  
Low Cost ◽  
Research Trends ◽  
Multirobot Systems ◽  
Future Research ◽  
Full Vehicle ◽  
Current State ◽  
Tremendous Progress ◽  
New Research

This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, high-speed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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