Climatic variability in Central Indian Himalaya during the last similar to 1800 years: Evidence from a high resolution speleothem record

Stable isotopes from a U/Th dated aragonite stalagmite from the Central Kumaun Himalaya provide evidence of variation in climatic conditions in the last similar to 1800 years. The delta O-18 and delta C-13 values vary from -4.3 parts per thousand to -7.6 parts per thousand and -3.4 parts per thousand to -9.1 parts per thousand respectively, although the stalagmite was not grown in isotopic equilibrium with cave drip water, a clear palaeoclimatic signal in stalagmite delta O-18 values is evident based on the regional climate data. The stalagmite showed a rapid growth rate during 830-910 AD, most likely the lower part of Medieval Warm Period (MWP), and 1600-1640 AD, the middle part of Little Ice Age (LIA). Two distinct phases of reduced precipitation are marked by a 2 parts per thousand shift in 8180 values towards the end of MWP (similar to 1080-1160 AD) and after its termination from similar to 1210 to 1440 AD. The LIA (similar to 1440-1880 AD) is represented by sub-tropical climate similar to modern conditions, whereas the post-LIA was comparatively drier. The Inter Tropical Convergence Zone (ITCZ) was located over the cave location during wetter/warmer conditions. When it shifted southward, precipitation over the study area decreased. A prominent drop in delta O-18 and delta C-13 values during the post-LIA period may also have been additionally influenced by anthropogenic activity in the area. (C) 2013 Elsevier Ltd and INQUA. All rights reserved.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.