On the Static Diffie-Hellman Problem on Elliptic Curves over Extension Fields

We show that for any elliptic curve E(Fqn ), if an adversary has access to a Static Diffie-Hellman Problem (Static DHP) oracle, then by making O(q1− 1/n+1) Static DHP oracle queries during an initial learning phase, for fixed n > 1 and q → ∞ the adversary can solve any further instance of the Static DHP in heuristic time O˜(q1− 1/n+1). Our proposal also solves the Delayed Target DHP as defined by Freeman, and naturally extends to provide algorithms for solving the Delayed Target DLP, the One-More DHP and One-More DLP, as studied by Koblitz and Menezes in the context of Jacobians of hyperelliptic curves of small genus. We also argue that for any group in which index calculus can be effectively applied, the above problems have a natural relationship, and will always be easier than the DLP. While practical only for very small n, our algorithm reduces the security provided by the elliptic curves defined over Fp2 and Fp4 proposed by Galbraith, Lin and Scott at EUROCRYPT 2009, should they be used in any protocol where a user can be made to act as a proxy Static DHP oracle, or if used in protocols whose security is related to any of the above problems

TRY plant trait database – enhanced coverage and open access

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

Small states: sport and politics at the margin

The aims of this article are the identification of the objectives of small states for participation in international sport and investing in elite sport and the analysis of the strategies that small states adopt to maximize their ability to achieve their sport and non-sport objectives. The article discusses the problem of the definition of smallness and explores objective, relative and subjective definitions. There follows an analysis of the characteristics of vulnerability and capacity in relation to strategy development and a review of the utility of international relations theory for the analysis of small states. The article continues with the identification of five possible strategies that might be adopted by small states (independent/autonomous, isomorphist/imitative, isolationist, collective and camp follower) and the resources needed to operationalize the strategies. Following a brief review of the examples of Singapore and Ireland, the article concludes that the scope for the adoption of independent strategies is likely to narrow given the global ambitions of international federations and the International Olympic Committee. The conclusion also considers the prospects for stimulating research in the area of small state sport policy and argues that the prospects are not good

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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