Balancing climate change mitigation and environmental protection interests in the EU Directive on carbon capture and storage

The EU Climate and Energy Package highlights the potential contradictions between the climate change imperative of reducing GHGs emissions and the importance to maintain environmental integrity. While the package supports climate change mainstreaming, it remains to be seen to what extent it succeeds in achieving internal environmental integration between climate change mitigation and other environment- protection objectives. Directive 2009/31/EC on the capture and geological storage of carbon dioxide (hereinafter the CCS Directive) offers a paradigmatic example of this potential conflict. One of the main regulatory challenges arising from the CCS Directive relates to finding the proper balance between the different interests involved and the not-fully-consistent objectives of environmental protection, climate change mitigation, and energy security. The present article will discuss this regulatory challenge and examine how the CCS Directive’s regulatory framework for CCS permits a combination of the various interests at stake and the giving of proper weight to concerns about environmental protection. The role that the precautionary principle in conjunction with the proportionality principle may have in balancing climate change mitigation and environment-protection interests will be considere

Oocyte cryopreservation as an adjunct to the assisted reproductive technologies

The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included. See page 2 of PDF for this item.Keith L Harrison, Michelle T Lane, Jeremy C Osborn, Christine A Kirby, Regan Jeffrey, John H Esler and David Mollo

A Standalone FPGA-based Miner for Lyra2REv2 Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms, and it is used as a proof-of-work function in several cryptocurrencies. The most crucial and exotic hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. This work presents the first hardware implementation of the specific instance of Lyra2 that is used in Lyra2REv2. Several properties of the aforementioned algorithm are exploited in order to optimize the design. In addition, an FPGA-based hardware implementation of a standalone miner for Lyra2REv2 on a Xilinx Multi-Processor System on Chip is presented. The proposed Lyra2REv2 miner is shown to be significantly more energy efficient than both a GPU and a commercially available FPGA-based miner. Finally, we also explain how the simplified Lyra2 and Lyra2REv2 architectures can be modified with minimal effort to also support the recent Lyra2REv3 chained hashing algorithm.Comment: 13 pages, accepted for publication in IEEE Trans. Circuits Syst. I. arXiv admin note: substantial text overlap with arXiv:1807.0576

A Lyra2 FPGA Core for Lyra2REv2-Based Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms and it is used as a proof-of-work function in several cryptocurrencies that aim to be ASIC-resistant. The most crucial hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. In this work we present the first FPGA implementation of the aforementioned instance of Lyra2 and we explain how several properties of the algorithm can be exploited in order to optimize the design.Comment: 5 pages, to be presented at the IEEE International Symposium on Circuits and Systems (ISCAS) 201

Learning the Structure for Structured Sparsity

Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets of variables during the subset selection process, which is not available in general. Inferring group weights from data is a key open research problem in structured sparsity.In this paper, we propose a Bayesian approach to the problem of group weight learning. We model the group weights as hyperparameters of heavy-tailed priors on groups of variables and derive an approximate inference scheme to infer these hyperparameters. We empirically show that we are able to recover the model hyperparameters when the data are generated from the model, and we demonstrate the utility of learning weights in synthetic and real denoising problems