Oliver Cromwell and the 'Cromwellian' Settlement of Ireland

Oliver Cromwell remains a deeply controversial figure in Ireland. In the past decade, his role in the conquest has received sustained attention. However, in recent scholarship on the settlement of Ireland in the 1650s, he has enjoyed a peculiarly low profile. This trend has served to compound the interpretative problems relating to Cromwell and Ireland which stem in part from the traditional denominational divide in Irish historiography. This article offers a reappraisal of Cromwell's role in designing and implementing the far-reaching ‘Cromwellian’ land settlement. It examines the evidence relating to his dealings with Irish people, both Protestant and Catholic, and his attitude towards the enormous difficulties which they faced post-conquest. While the massacre at Drogheda in 1649 remains a blot on his reputation, in the 1650s Cromwell in fact emerged as an important and effective ally for Irish landowners seeking to defeat the punitive confiscation and transplantation policies approved by the Westminster parliament and favoured by the Dublin government.

Sustained effectiveness of a multifaceted intervention to reduce potentially inappropriate prescribing in older patients in primary care (OPTI-SCRIPT study)

Background: Potentially inappropriate prescribing (PIP) is common in older people in primary care and can result in increased morbidity, adverse drug events and hospitalisations. We previously demonstrated the success of a multifaceted intervention in decreasing PIP in primary care in a cluster randomised controlled trial (RCT).
Objective: We sought to determine whether the improvement in PIP in the short term was sustained at 1-year follow-up.
Methods: A cluster RCT was conducted with 21 GP practices and 196 patients (aged ≥70) with PIP in Irish primary care. Intervention participants received a complex multifaceted intervention incorporating academic detailing, medicine review with web-based pharmaceutical treatment algorithms that provide recommended alternative treatment options, and tailored patient information leaflets. Control practices delivered usual care and received simple, patient-level PIP feedback. Primary outcomes were the proportion of patients with PIP and the mean number of potentially inappropriate prescriptions at 1-year follow-up. Intention-to-treat analysis using random effects regression was used.
Results: All 21 GP practices and 186 (95 %) patients were followed up. We found that at 1-year follow-up, the significant reduction in the odds of PIP exposure achieved during the intervention was sustained after its discontinuation (adjusted OR 0.28, 95 % CI 0.11 to 0.76, P = 0.01). Intervention participants had significantly lower odds of having a potentially inappropriate proton pump inhibitor compared to controls (adjusted OR 0.40, 95 % CI 0.17 to 0.94, P = 0.04).
Conclusion: The significant reduction in the odds of PIP achieved during the intervention was sustained after its discontinuation. These results indicate that improvements in prescribing quality can be maintained over time.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

A Real-Time Synthesis Oriented Tanpura Model

Physics-based synthesis of tanpura drones requires accurate simulation of stiff, lossy string vibrations while incorporating sustained contact with the bridge and a cotton thread. Several challenges arise from this when seeking efficient and stable algorithms for real-time sound synthesis. The approach proposed here to address these combines modal expansion of the string dynamics with strategic simplifications regarding the string-bridge and string-thread contact, resulting in an efficient and provably stable time-stepping scheme with exact modal parameters. Attention is given also to the physical characterisation of the system, including string damping behaviour, body radiation characteristics, and determination of appropriate contact parameters. Simulation results are presented exemplifying the key features of the model.