University of Oxford

About: University of Oxford is a education organization based out in Oxford, Oxfordshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 99713 authors who have published 258108 publications receiving 12972806 citations. The organization is also known as: Oxford University & Oxon..

Ensemble forecasting of species distributions

Abstract: Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

Automated Flower Classification over a Large Number of Classes

Abstract: We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.

Recent advances in the synthesis and application of layered double hydroxide (LDH) nanosheets.

Abstract: Layered double hydroxides (LDHs) are a class of ionic lamellar compounds made up of positively charged brucite-like layers with an interlayer region containing charge compensating anions and solvation molecules. Delamination of LDHs is an interesting route for producing positively charged thin platelets with a thickness of a few atomic layers, which can be used as nanocomposites for polymers or as building units for making new designed organic-inorganic or inorganic-inorganic nanomaterials. The synthesis of nanosized LDH platelets can be generally classified into two approaches, bottom-up and top-down. It requires modification of the LDH interlamellar environment and then selection of an appropriate solvent system. In DDS intercalated LDHs, the aliphatic tails of the DDS- anions exhibit a high degree of interdigitation in order to maximize guest-guest dispersive interactions. Bellezza reported that the LDH colloids can also been obtained by employing a reverse microemulsion approach.