Editor's Introduction Governments across the world are formulating and implementing medical, soci... more Editor's Introduction Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate.
The breadth of biodiversity literature available through the Biodiversity
Heritage Library (BHL)... more The breadth of biodiversity literature available through the Biodiversity
Heritage Library (BHL) is potentially of great use to agricultural research.
It provides access to literature drawn from across the world, and its archives
document the Earth as it was one hundred years ago and more. However, this
strength of BHL is also its weakness: the breadth of coverage of BHL can
complicate finding relevant literature. In this short paper, we will explore the
practical issues arising from attempting to filter out relevant legacy literature to
support agricultural research.
agINFRA EU FP7 e-Infrastructure project -Grant agreement n°283770 www.aginfra.eu Aims and Objecti... more agINFRA EU FP7 e-Infrastructure project -Grant agreement n°283770 www.aginfra.eu Aims and Objectives agINFRA will design and develop a scientific data infrastructure for agricultural science to facilitate the development of policies and the deployment of services that promote sharing of data among agricultural scientists and develop trust within and among the community.
In the context of the need for massive free education for the Complex Systems Society and the
UNE... more In the context of the need for massive free education for the Complex Systems Society and the UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of thousands of students’ work for certification1. Automated marking is a partial solution but has many drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since every extra student is an extra marker. However there are concerns about the quality of peer marking, since some students may not be competent to mark the work of others. Some students are better than others and often the best students are well qualified to assess the work of their peers. To make peer marking high quality we are using new hypernetwork-based methods to extend previous methods2 to discover which students are good markers and which students are less good as a course progresses.
Education is a major force for economic and social wellbeing.
Despite high aspirations, educatio... more Education is a major force for economic and social wellbeing.
Despite high aspirations, education at all levels can be expensive and
ineffective. Three Grand Challenges are identified: (1) enable people to
learn orders of magnitude more effectively, (2) enable people to learn
at orders of magnitude less cost, and (3) demonstrate success by exemplary
interdisciplinary education in complex systems science. A ten year
‘man-on-the-moon’ project is proposed in which FuturICT’s unique
combination of Complexity, Social and Computing Sciences could provide
an urgently needed transdisciplinary language for making sense
of educational systems. In close dialogue with educational theory and
practice, and grounded in the emerging data science and learning analytics
paradigms, this will translate into practical tools (both analytical
and computational) for researchers, practitioners and leaders; generative
principles for resilient educational ecosystems; and innovation for
radically scalable, yet personalised, learner engagement and assessment.
The proposed Education Accelerator will serve as a ‘wind tunnel’ for
testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education
exploiting the new understanding of complex, social, computationally
enhanced organisational structure developed within FuturICT.
A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mai... more A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.>
We report on progress in handwriting recognition and signature verication. Our system, which uses... more We report on progress in handwriting recognition and signature verication. Our system, which uses pen-trajectory information, is suitable for use in pen-based computers. It has a multi-modular architecture whose central trainable module is a Time Delay Neural Network. Results comparing our system and a commercial recognizer are presented. Our best recognizer make three times less errors on hand-printed word recognition than the commercial one.
A neural network with 136000 connections for recognition of handwritten digits has been implement... more A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision
The architecture, implementation, and applications of a special-purpose neural network processor ... more The architecture, implementation, and applications of a special-purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its data path is particularly suitable for the convolutional topologies that are typical in classification networks, but can also be configured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6-b accuracy for the weights and 3 b for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130,000 connections and was evaluated in 1 ms.
The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laborat... more The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laboratories, including a recognition network that learns feature extraction kernels and a custom VLSI chip that is designed for neural-net image processing. It is concluded that both high speed and high accuracy can be obtained using neural-net methods for character recognition. Networks can be designed that learn their own feature extraction kernels. Special-purpose neural-net chips combined with digital signal processors can quickly evaluate character-recognition neural nets. This high speed is particularly useful for recognition-based segmentation of character strings.>
A high-speed programmable neural network chip and its application to character recognition are de... more A high-speed programmable neural network chip and its application to character recognition are described. A network with over 130000 connections has been implemented on a single chip and operates at a rate of over 1000 classifications per second. The chip performs up to 2000 multiplications and additions simultaneously. Its datapath is suitable for the convolutional architectures that are typical in pattern classification networks, but can also be configured for fully connected or feedback topologies. Computations were performed with 6 bits accuracy for the weights and 3 bits for the states. The chip uses analog processing internally for higher density and reduced power dissipation, but all input/output is digital to simplify system integration
A proven strength of neural-network methods is their application to character recognition and doc... more A proven strength of neural-network methods is their application to character recognition and document analysis. In this paper we describe a neural-net Optical Character Recognizer (OCR), neural-net preprocessing, and neural-net hardware accelerators that together comprise a high-performance character recognition system. We also describe applications in network-based fax and bit-mapped text processing.
We report the results of an experimental study on normal and amblyopic vision, in which we have m... more We report the results of an experimental study on normal and amblyopic vision, in which we have measured spatial filer characteristics and pattern discrimination performance.
Editor's Introduction Governments across the world are formulating and implementing medical, soci... more Editor's Introduction Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate.
The breadth of biodiversity literature available through the Biodiversity
Heritage Library (BHL)... more The breadth of biodiversity literature available through the Biodiversity
Heritage Library (BHL) is potentially of great use to agricultural research.
It provides access to literature drawn from across the world, and its archives
document the Earth as it was one hundred years ago and more. However, this
strength of BHL is also its weakness: the breadth of coverage of BHL can
complicate finding relevant literature. In this short paper, we will explore the
practical issues arising from attempting to filter out relevant legacy literature to
support agricultural research.
agINFRA EU FP7 e-Infrastructure project -Grant agreement n°283770 www.aginfra.eu Aims and Objecti... more agINFRA EU FP7 e-Infrastructure project -Grant agreement n°283770 www.aginfra.eu Aims and Objectives agINFRA will design and develop a scientific data infrastructure for agricultural science to facilitate the development of policies and the deployment of services that promote sharing of data among agricultural scientists and develop trust within and among the community.
In the context of the need for massive free education for the Complex Systems Society and the
UNE... more In the context of the need for massive free education for the Complex Systems Society and the UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of thousands of students’ work for certification1. Automated marking is a partial solution but has many drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since every extra student is an extra marker. However there are concerns about the quality of peer marking, since some students may not be competent to mark the work of others. Some students are better than others and often the best students are well qualified to assess the work of their peers. To make peer marking high quality we are using new hypernetwork-based methods to extend previous methods2 to discover which students are good markers and which students are less good as a course progresses.
Education is a major force for economic and social wellbeing.
Despite high aspirations, educatio... more Education is a major force for economic and social wellbeing.
Despite high aspirations, education at all levels can be expensive and
ineffective. Three Grand Challenges are identified: (1) enable people to
learn orders of magnitude more effectively, (2) enable people to learn
at orders of magnitude less cost, and (3) demonstrate success by exemplary
interdisciplinary education in complex systems science. A ten year
‘man-on-the-moon’ project is proposed in which FuturICT’s unique
combination of Complexity, Social and Computing Sciences could provide
an urgently needed transdisciplinary language for making sense
of educational systems. In close dialogue with educational theory and
practice, and grounded in the emerging data science and learning analytics
paradigms, this will translate into practical tools (both analytical
and computational) for researchers, practitioners and leaders; generative
principles for resilient educational ecosystems; and innovation for
radically scalable, yet personalised, learner engagement and assessment.
The proposed Education Accelerator will serve as a ‘wind tunnel’ for
testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education
exploiting the new understanding of complex, social, computationally
enhanced organisational structure developed within FuturICT.
A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mai... more A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.>
We report on progress in handwriting recognition and signature verication. Our system, which uses... more We report on progress in handwriting recognition and signature verication. Our system, which uses pen-trajectory information, is suitable for use in pen-based computers. It has a multi-modular architecture whose central trainable module is a Time Delay Neural Network. Results comparing our system and a commercial recognizer are presented. Our best recognizer make three times less errors on hand-printed word recognition than the commercial one.
A neural network with 136000 connections for recognition of handwritten digits has been implement... more A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision
The architecture, implementation, and applications of a special-purpose neural network processor ... more The architecture, implementation, and applications of a special-purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its data path is particularly suitable for the convolutional topologies that are typical in classification networks, but can also be configured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6-b accuracy for the weights and 3 b for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130,000 connections and was evaluated in 1 ms.
The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laborat... more The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laboratories, including a recognition network that learns feature extraction kernels and a custom VLSI chip that is designed for neural-net image processing. It is concluded that both high speed and high accuracy can be obtained using neural-net methods for character recognition. Networks can be designed that learn their own feature extraction kernels. Special-purpose neural-net chips combined with digital signal processors can quickly evaluate character-recognition neural nets. This high speed is particularly useful for recognition-based segmentation of character strings.>
A high-speed programmable neural network chip and its application to character recognition are de... more A high-speed programmable neural network chip and its application to character recognition are described. A network with over 130000 connections has been implemented on a single chip and operates at a rate of over 1000 classifications per second. The chip performs up to 2000 multiplications and additions simultaneously. Its datapath is suitable for the convolutional architectures that are typical in pattern classification networks, but can also be configured for fully connected or feedback topologies. Computations were performed with 6 bits accuracy for the weights and 3 bits for the states. The chip uses analog processing internally for higher density and reduced power dissipation, but all input/output is digital to simplify system integration
A proven strength of neural-network methods is their application to character recognition and doc... more A proven strength of neural-network methods is their application to character recognition and document analysis. In this paper we describe a neural-net Optical Character Recognizer (OCR), neural-net preprocessing, and neural-net hardware accelerators that together comprise a high-performance character recognition system. We also describe applications in network-based fax and bit-mapped text processing.
We report the results of an experimental study on normal and amblyopic vision, in which we have m... more We report the results of an experimental study on normal and amblyopic vision, in which we have measured spatial filer characteristics and pattern discrimination performance.
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Papers by Jane Bromley
Heritage Library (BHL) is potentially of great use to agricultural research.
It provides access to literature drawn from across the world, and its archives
document the Earth as it was one hundred years ago and more. However, this
strength of BHL is also its weakness: the breadth of coverage of BHL can
complicate finding relevant literature. In this short paper, we will explore the
practical issues arising from attempting to filter out relevant legacy literature to
support agricultural research.
UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of
thousands of students’ work for certification1. Automated marking is a partial solution but has many
drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since
every extra student is an extra marker. However there are concerns about the quality of peer marking,
since some students may not be competent to mark the work of others. Some students are better than
others and often the best students are well qualified to assess the work of their peers. To make peer
marking high quality we are using new hypernetwork-based methods to extend previous methods2 to
discover which students are good markers and which students are less good as a course progresses.
Despite high aspirations, education at all levels can be expensive and
ineffective. Three Grand Challenges are identified: (1) enable people to
learn orders of magnitude more effectively, (2) enable people to learn
at orders of magnitude less cost, and (3) demonstrate success by exemplary
interdisciplinary education in complex systems science. A ten year
‘man-on-the-moon’ project is proposed in which FuturICT’s unique
combination of Complexity, Social and Computing Sciences could provide
an urgently needed transdisciplinary language for making sense
of educational systems. In close dialogue with educational theory and
practice, and grounded in the emerging data science and learning analytics
paradigms, this will translate into practical tools (both analytical
and computational) for researchers, practitioners and leaders; generative
principles for resilient educational ecosystems; and innovation for
radically scalable, yet personalised, learner engagement and assessment.
The proposed Education Accelerator will serve as a ‘wind tunnel’ for
testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education
exploiting the new understanding of complex, social, computationally
enhanced organisational structure developed within FuturICT.
Heritage Library (BHL) is potentially of great use to agricultural research.
It provides access to literature drawn from across the world, and its archives
document the Earth as it was one hundred years ago and more. However, this
strength of BHL is also its weakness: the breadth of coverage of BHL can
complicate finding relevant literature. In this short paper, we will explore the
practical issues arising from attempting to filter out relevant legacy literature to
support agricultural research.
UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of
thousands of students’ work for certification1. Automated marking is a partial solution but has many
drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since
every extra student is an extra marker. However there are concerns about the quality of peer marking,
since some students may not be competent to mark the work of others. Some students are better than
others and often the best students are well qualified to assess the work of their peers. To make peer
marking high quality we are using new hypernetwork-based methods to extend previous methods2 to
discover which students are good markers and which students are less good as a course progresses.
Despite high aspirations, education at all levels can be expensive and
ineffective. Three Grand Challenges are identified: (1) enable people to
learn orders of magnitude more effectively, (2) enable people to learn
at orders of magnitude less cost, and (3) demonstrate success by exemplary
interdisciplinary education in complex systems science. A ten year
‘man-on-the-moon’ project is proposed in which FuturICT’s unique
combination of Complexity, Social and Computing Sciences could provide
an urgently needed transdisciplinary language for making sense
of educational systems. In close dialogue with educational theory and
practice, and grounded in the emerging data science and learning analytics
paradigms, this will translate into practical tools (both analytical
and computational) for researchers, practitioners and leaders; generative
principles for resilient educational ecosystems; and innovation for
radically scalable, yet personalised, learner engagement and assessment.
The proposed Education Accelerator will serve as a ‘wind tunnel’ for
testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education
exploiting the new understanding of complex, social, computationally
enhanced organisational structure developed within FuturICT.