| 1. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 2. |
- Aarrestad, Thea, et al.
(författare)
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Benchmark data and model independent event classification for the large hadron collider
- 2022
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Ingår i: SciPost Physics. - 2542-4653. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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| 3. |
- Famelis, Panagiotis, et al.
(författare)
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P5: Event-driven Policy Framework for P4-based Traffic Engineering
- 2023
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Ingår i: Proceedings of the 24th International Conference on High Performance Switching and Routing. - 9781665476409
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Konferensbidrag (refereegranskat)abstract
- We present P5, an event-driven policy framework that allows network operators to realize end-to-end policies on top of P4-based data planes in an intuitive and effective manner. We demonstrate how P5 adheres to a service-level agreement (SLA) by applying P4-based traffic engineering with latency constraints.
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| 4. |
- Vilalta, Ricard, et al.
(författare)
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TeraFlow: Secured autonomic traffic management for a tera of SDN flows
- 2021
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Ingår i: 2021 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2021. - 9781665415262 ; , s. 377-382
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Konferensbidrag (refereegranskat)abstract
- TeraFlow proposes a new type of secure, cloud-native Software Defined Networking (SDN) controller that will radically advance the state-of-the-art in beyond 5G networks by introducing novel micro-services architecture, and provide revolutionary features for both flow management (service layer) and optical/microwave network equipment integration (infras-tructure layer) by adapting new data models. TeraFlow will also incorporate security using Machine Learning (ML) and forensic evidence for multi-tenancy based on Distributed Ledgers. Finally, this new SDN controller shall be able to integrate with the current Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) frameworks as well as to other networks. The target pool of TeraFlow stakeholders expands beyond the traditional telecom operators towards edge and hyperscale cloud providers.
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