Evaluation and optimisation of interface force fields for water on gold surfaces

The structure and dynamics of water at gold surfaces is important for a variety of applications, including lab on a chip and electrowetting. Classical molecular dynamics (MD) simulations are frequently used to investigate systems with water-gold interfaces, such as biomacromolecules in gold nanoparticle dispersions, but the accuracy of the simulations depends on the suitability of the force field. Density functional theory (DFT) calculations of a water molecule on gold were used as a benchmark to assess force field accuracy. It was found that Lennard-Jones potentials did not reproduce the DFT water-gold configurational energy landscape whereas the softer Morse and Buckingham potentials allowed for a more accurate representation. MD simulations with different force fields exhibited rather different structural and dynamic properties of water on a gold surface. This emphasises the need for experimental data and further effort on the validation of a realistic force field for water-gold interactions

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

MARS Bulletin Vol.21 No. 11

MARS Bulletin Vol.21 No. 11JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 13

MARS Bulletin Vol.21 No. 13 CAMPAIGN REVIEWJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.22 No 10 (2014) - High summer crop yields at EU-28 level

Crop Monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.22 No 12 (2014) - Weakly hardened crops in western and central Europe

Crop Monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.22 No 11 (2014) - Predominantly favourable sowing conditions despite some delays

Crop Monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 10

MARS Bulletin Vol.21 No. 10JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol. 23 No 4 (2015) - Continued positive outlook

Crop monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol. 23 No 3 (2015) - Current outlook is predominantly positive

Crop monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource