A study of patent thickets

Report analysing whether entry of UK enterprises into patenting in a technology area is affected by patent thickets in the technology area

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts

Evolutionary multi-objective training set selection of data instances and augmentations for vocal detection

© Springer Nature Switzerland AG 2019. The size of publicly available music data sets has grown significantly in recent years, which allows training better classification models. However, training on large data sets is time-intensive and cumbersome, and some training instances might be unrepresentative and thus hurt classification performance regardless of the used model. On the other hand, it is often beneficial to extend the original training data with augmentations, but only if they are carefully chosen. Therefore, identifying a “smart” selection of training instances should improve performance. In this paper, we introduce a novel, multi-objective framework for training set selection with the target to simultaneously minimise the number of training instances and the classification error. Experimentally, we apply our method to vocal activity detection on a multi-track database extended with various audio augmentations for accompaniment and vocals. Results show that our approach is very effective at reducing classification error on a separate validation set, and that the resulting training set selections either reduce classification error or require only a small fraction of training instances for comparable performance

Does Public Attention Reduce the Influence of Moneyed Interests? Policy Positions on SOPA/PIPA Before and After the Internet Blackout

We investigate the role of public attention in determining the effect that campaign contributions by interest groups have on legislators' policy positions. We exploit the shock in public attention induced by the Internet service blackout of January 2012 that increased the salience of the SOPA/PIPA bills aimed at stronger protection of property rights on the Internet. Using a new dataset of U.S. congressmen's public statements, we find a strong statistical relationship between campaign contributions funded by the affected industries and legislators' positions. However, this relationship evaporates once the two bills become primary policy issues. Our results are consistent with the notion that legislators choose positions on secondary policy issues in order to cater to organized interests, whereas positions on primary policy issues are driven by electoral support