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2,972 Projects

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  • Funder: NIH Project Code: 5R01DA031043-05
    Funder Contribution: 1 USD
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  • Funder: EC Project Code: 244422
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  • Funder: EC Project Code: 265156
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  • Funder: NSF Project Code: 9530098
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  • Funder: UKRI Project Code: NE/K00008X/2
    Funder Contribution: 42,744 GBP

    Submarine landslides can be far larger than terrestrial landslides, and many generate destructive tsunamis. The Storegga Slide offshore Norway covers an area larger than Scotland and contains enough sediment to cover all of Scotland to a depth of 80 m. This huge slide occurred 8,200 years ago and extends for 800 km down slope. It produced a tsunami with a run up >20 m around the Norwegian Sea and 3-8 m on the Scottish mainland. The UK faces few other natural hazards that could cause damage on the scale of a repeat of the Storegga Slide tsunami. The Storegga Slide is not the only huge submarine slide in the Norwegian Sea. Published data suggest that there have been at least six such slides in the last 20,000 years. For instance, the Traenadjupet Slide occurred 4,000 years ago and involved ~900 km3 of sediment. Based on a recurrence interval of 4,000 years (2 events in the last 8,000 years, or 6 events in 20,000 years), there is a 5% probability of a major submarine slide, and possible tsunami, occurring in the next 200 years. Sedimentary deposits in Shetland dated at 1500 and 5500 years, in addition to the 8200 year Storegga deposit, are thought to indicate tsunami impacts and provide evidence that the Arctic tsunami hazard is still poorly understood. Given the potential impact of tsunamis generated by Arctic landslides, we need a rigorous assessment of the hazard they pose to the UK over the next 100-200 years, their potential cost to society, degree to which existing sea defences protect the UK, and how tsunami hazards could be incorporated into multi-hazard flood risk management. This project is timely because rapid climatic change in the Arctic could increase the risk posed by landslide-tsunamis. Crustal rebound associated with future ice melting may produce larger and more frequent earthquakes, such as probably triggered the Storegga Slide 8200 years ago. The Arctic is also predicted to undergo particularly rapid warming in the next few decades that could lead to dissociation of gas hydrates (ice-like compounds of methane and water) in marine sediments, weakening the sediment and potentially increasing the landsliding risk. Our objectives will be achieved through an integrated series of work blocks that examine the frequency of landslides in the Norwegian Sea preserved in the recent geological record, associated tsunami deposits in Shetland, future trends in frequency and size of earthquakes due to ice melting, slope stability and tsunami generation by landslides, tsunami inundation of the UK and potential societal costs. This forms a work flow that starts with observations of past landslides and evolves through modelling of their consequences to predicting and costing the consequences of potential future landslides and associated tsunamis. Particular attention will be paid to societal impacts and mitigation strategies, including examination of the effectiveness of current sea defences. This will be achieved through engagement of stakeholders from the start of the project, including government agencies that manage UK flood risk, international bodies responsible for tsunami warning systems, and the re-insurance sector. The main deliverables will be: (i) better understanding of frequency of past Arctic landslides and resulting tsunami impact on the UK (ii) improved models for submarine landslides and associated tsunamis that help to understand why certain landslides cause tsunamis, and others don't. (iii) a single modelling strategy that starts with a coupled landslide-tsunami source, tracks propagation of the tsunami across the Norwegian Sea, and ends with inundation of the UK coast. Tsunami sources of various sizes and origins will be tested (iv) a detailed evaluation of the consequences and societal cost to the UK of tsunami flooding , including the effectiveness of existing flood defences (v) an assessment of how climate change may alter landslide frequency and thus tsunami risk to the UK.

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  • Funder: NIH Project Code: 1F33DE020006-01
    Funder Contribution: 8,592 USD
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  • Funder: NIH Project Code: 2R01RR007127-04
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  • Funder: SNSF Project Code: 107307
    Funder Contribution: 21,875
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  • Funder: NIH Project Code: 5R01NS033491-02
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  • Funder: CHIST-ERA Project Code: M2CR

    Communication is one of the necessary condition to develop intelligence in living beings. Humans use several modalities to exchange information: speech, written text, both in many languages, gestures, images, and many more. There is evidence that human learning is more effective when several modalities are used. There is a large body of research to make computers process these modalities, and ultimately, understand human language. These modalities have been, however, generally addressed independently or at most in pairs. However, merging information from multiple modalities is best done at the highest levels of abstraction, which deep learning models are trained to capture. The M2CR project aims at developing a revolutionary approach to combine all these modalities and their respective tasks in one unified architecture, based on deep neural networks, including both a discriminant and a generative component through multiple levels of representation. Our system will jointly learn from resources in several modalities, including but not limited to text of several languages (European languages, Chinese and Arabic), speech and images. In doing so, the system will learn one common semantic representation of the underlying information, both at a channel-specific level and at a higher channel-independent level. Pushing these ideas to the large scale, e.g. training on very large corpora, the M2CR project has the ambition to advance the state-of-the-art in human language understanding (HLU). M2CR will address all major tasks in HLU by one unified architecture: speech understanding and translation, multilingual image retrieval and description, etc. The M2CR project will collect existing multimodal and multilingual corpora, extend them as needed, and make them freely available to the community. M2CR will also define shared tasks to set up a common evaluation framework and ease research for other institutions, beyond the partners of this consortium. All developed software and tools will be open-source. By these means, we hope to help to advance the field of human language.

    visibility23
    visibilityviews23
    downloaddownloads84
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Advanced search in
Projects
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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
2,972 Projects
  • Funder: NIH Project Code: 5R01DA031043-05
    Funder Contribution: 1 USD
    more_vert
  • Funder: EC Project Code: 244422
    more_vert
  • Funder: EC Project Code: 265156
    visibility193
    visibilityviews193
    downloaddownloads377
    Powered by Usage counts
    more_vert
  • Funder: NSF Project Code: 9530098
    more_vert
  • Funder: UKRI Project Code: NE/K00008X/2
    Funder Contribution: 42,744 GBP

    Submarine landslides can be far larger than terrestrial landslides, and many generate destructive tsunamis. The Storegga Slide offshore Norway covers an area larger than Scotland and contains enough sediment to cover all of Scotland to a depth of 80 m. This huge slide occurred 8,200 years ago and extends for 800 km down slope. It produced a tsunami with a run up >20 m around the Norwegian Sea and 3-8 m on the Scottish mainland. The UK faces few other natural hazards that could cause damage on the scale of a repeat of the Storegga Slide tsunami. The Storegga Slide is not the only huge submarine slide in the Norwegian Sea. Published data suggest that there have been at least six such slides in the last 20,000 years. For instance, the Traenadjupet Slide occurred 4,000 years ago and involved ~900 km3 of sediment. Based on a recurrence interval of 4,000 years (2 events in the last 8,000 years, or 6 events in 20,000 years), there is a 5% probability of a major submarine slide, and possible tsunami, occurring in the next 200 years. Sedimentary deposits in Shetland dated at 1500 and 5500 years, in addition to the 8200 year Storegga deposit, are thought to indicate tsunami impacts and provide evidence that the Arctic tsunami hazard is still poorly understood. Given the potential impact of tsunamis generated by Arctic landslides, we need a rigorous assessment of the hazard they pose to the UK over the next 100-200 years, their potential cost to society, degree to which existing sea defences protect the UK, and how tsunami hazards could be incorporated into multi-hazard flood risk management. This project is timely because rapid climatic change in the Arctic could increase the risk posed by landslide-tsunamis. Crustal rebound associated with future ice melting may produce larger and more frequent earthquakes, such as probably triggered the Storegga Slide 8200 years ago. The Arctic is also predicted to undergo particularly rapid warming in the next few decades that could lead to dissociation of gas hydrates (ice-like compounds of methane and water) in marine sediments, weakening the sediment and potentially increasing the landsliding risk. Our objectives will be achieved through an integrated series of work blocks that examine the frequency of landslides in the Norwegian Sea preserved in the recent geological record, associated tsunami deposits in Shetland, future trends in frequency and size of earthquakes due to ice melting, slope stability and tsunami generation by landslides, tsunami inundation of the UK and potential societal costs. This forms a work flow that starts with observations of past landslides and evolves through modelling of their consequences to predicting and costing the consequences of potential future landslides and associated tsunamis. Particular attention will be paid to societal impacts and mitigation strategies, including examination of the effectiveness of current sea defences. This will be achieved through engagement of stakeholders from the start of the project, including government agencies that manage UK flood risk, international bodies responsible for tsunami warning systems, and the re-insurance sector. The main deliverables will be: (i) better understanding of frequency of past Arctic landslides and resulting tsunami impact on the UK (ii) improved models for submarine landslides and associated tsunamis that help to understand why certain landslides cause tsunamis, and others don't. (iii) a single modelling strategy that starts with a coupled landslide-tsunami source, tracks propagation of the tsunami across the Norwegian Sea, and ends with inundation of the UK coast. Tsunami sources of various sizes and origins will be tested (iv) a detailed evaluation of the consequences and societal cost to the UK of tsunami flooding , including the effectiveness of existing flood defences (v) an assessment of how climate change may alter landslide frequency and thus tsunami risk to the UK.

    more_vert
  • Funder: NIH Project Code: 1F33DE020006-01
    Funder Contribution: 8,592 USD
    more_vert
  • Funder: NIH Project Code: 2R01RR007127-04
    more_vert
  • Funder: SNSF Project Code: 107307
    Funder Contribution: 21,875
    more_vert
  • Funder: NIH Project Code: 5R01NS033491-02
    more_vert
  • Funder: CHIST-ERA Project Code: M2CR

    Communication is one of the necessary condition to develop intelligence in living beings. Humans use several modalities to exchange information: speech, written text, both in many languages, gestures, images, and many more. There is evidence that human learning is more effective when several modalities are used. There is a large body of research to make computers process these modalities, and ultimately, understand human language. These modalities have been, however, generally addressed independently or at most in pairs. However, merging information from multiple modalities is best done at the highest levels of abstraction, which deep learning models are trained to capture. The M2CR project aims at developing a revolutionary approach to combine all these modalities and their respective tasks in one unified architecture, based on deep neural networks, including both a discriminant and a generative component through multiple levels of representation. Our system will jointly learn from resources in several modalities, including but not limited to text of several languages (European languages, Chinese and Arabic), speech and images. In doing so, the system will learn one common semantic representation of the underlying information, both at a channel-specific level and at a higher channel-independent level. Pushing these ideas to the large scale, e.g. training on very large corpora, the M2CR project has the ambition to advance the state-of-the-art in human language understanding (HLU). M2CR will address all major tasks in HLU by one unified architecture: speech understanding and translation, multilingual image retrieval and description, etc. The M2CR project will collect existing multimodal and multilingual corpora, extend them as needed, and make them freely available to the community. M2CR will also define shared tasks to set up a common evaluation framework and ease research for other institutions, beyond the partners of this consortium. All developed software and tools will be open-source. By these means, we hope to help to advance the field of human language.

    visibility23
    visibilityviews23
    downloaddownloads84
    Powered by Usage counts
    more_vert