Abstract

The invention provides a method for manufacturing supported noble metal based alloy catalysts with a high degree of alloying and a small crystallite size. The method involves using polyol solvents as reaction medium and comprises a two-step reduction process in the presence of a support material. In the first step, the first metal (transition metal; eg Co, Cr, Ru) is activated by increasing the reaction temperature to 80 to 160 C. In the second step, the second metal (noble metal; eg Pt, Pd, Au) is added and the slurry is heated to the boiling point of the polyol solvent in a range of 160 to 300 C. The catalysts manufactured according to the method are used as electrocatalysts for polymer electrolyte membrane fuel cells (PEMFC), direct-methanol fuel cells (DMFC) or as gas phase catalysts for CO oxidation or exhaust gas purification.

Abstract

The combination of molecular dynamics simulation and neutron scattering techniques has emerged as a highly synergistic approach to elucidate the atomistic details of the structure, dynamics, and functions of biological systems. Simulation models can be tested by calculating neutron scattering structure factors and comparing the results directly with experiments. If the scattering profiles agree, the simulations can be used to provide a detailed decomposition and interpretation of the experiments, and if not, the models can be rationally adjusted. Comparison with neutron experiment can be made at the level of the scattering functions or, less directly, of structural and dynamical quantities derived from them. Here, we examine the combination of simulation and experiment in the interpretation of SANS and inelastic scattering experiments on the structure and dynamics of proteins and other biopolymers.

Abstract

An analytical approach is developed for reconstructing site-specific methyl-bearing protein side-chain conformational energy surfaces from NMR methyl axis order parameters (Oaxis2). Application of an enhanced sampling algorithm (adaptive biasing force) to molecular dynamics simulation of a protein, calcium-bound calmodulin, reveals a nonlinear correlation between Oaxis2 and the populations of rotamer states of protein side-chains, permitting the rotamer populations to be extracted directly from Oaxis2. The analytical approach yields side-chain conformational distributions that are in excellent agreement with those obtained from the enhanced-sampling MD results.

Abstract

With the recent emergence of cloud computing based services on the Internet, MapReduce and distributed file systems like HDFS have emerged as the paradigm of choice for developing large scale data intensive applications. Given the scale at which these applications are deployed, minimizing power consumption of these clusters can significantly cut down operational costs and reduce their carbon footprint—thereby increasing the utility from a provider’s point of view. This paper addresses energy conservation for clusters of nodes that run MapReduce jobs. The algorithm dynamically reconfigures the cluster based on the current workload and turns cluster nodes on or off when the average cluster utilization rises above or falls below administrator specified thresholds, respectively. We evaluate our algorithm using the GridSim toolkit and our results show that the proposed algorithm achieves an energy reduction of 33% under average workloads and up to 54% under low workloads.

Abstract

With the induction of UTF-8 unicode standards, web content in Hindi language is increasing at a rapid pace. There is a great opportunity to mine the content and get insight of sentiments and opinions expressed by people and various communities. In this paper, we present a graph based method to build a subjective lexicon for Hindi language, using WordNet as a resource. Our method takes a pre-annotated seed list and expands this list into a full lexicon using synonym and antonym relations. We show two different evaluation strategies to validate the Hindi Lexicon built. Main contribution of our work 1) Developing a Subjective lexicon of adjectives using Hindi WordNet. 2) Developing an annotated corpora of Hindi reviews.

Abstract

In this paper, we report our participation in Knowledge Base Population at TAC 2012. We adopted an Information Retrieval based approach for the Entity Linking and Slot Filling tasks. In Entity Linking we identify potential nodes from the Knowledge Base and then identify the mapping node using tf-idf similarity. We achieved very good performance in the Entity Linking task. For Slot Filling task we identify documents from the document collection that might contain attribute information. We extract the attribute information using a rule based approach. Our rule based approach hasnt performed up to the mark.

Abstract

The number of images on the World Wide Web has been increasing tremendously. Providing search services for images on the web has been an active research area. Web images are often surrounded by different associated texts like ALT text, surrounding text, image filename, html page title etc. Many popular internet search engines make use of these associated texts while indexing images and give higher importance to the terms present in ALT text. But, a recent study has shown that around half of the images on the web have no ALT text. So, predicting the ALT text of an image in a web page would be of great use in web image retrieval. We propose an approach on top of term cooccurrence approach proposed in the literature to ALT text prediction. Our results show that our approach and the simple term co-occurrence approach produce almost the same results. We analyze both the methods and describe the usage of the methods in different situations. We build an image annotation system on top of our proposed approach and compare the results with the image annotation system built on top of the term co-occurrence approach. Preliminary experiments on a set of 1000 images show that our proposed approach performs well over the simple term co-occurrence approach for web image annotation.

Abstract

With recent developments in web technologies, percentage web content in Hindi is growing up at a lighting speed. This information can prove to be very useful for researchers, governments and organization to learn what’s on public mind, to make sound decisions. In this paper, we present a graph based wordnet expansion method to generate a full (adjective and adverb) subjective lexicon. We used synonym and antonym relations to expand the initial seed lexicon. We show three different evaluation strategies to validate the lexicon. We achieve 70.4% agreement with human annotators and∼ 79% accuracy on product review classification. Main contribution of our work

Abstract

Web page classification has wide number of applications in the area of Information Retrieval. It is a crucial part in building domain specific search engines. Be it’Google Scholar’to search for scholarly articles or’Google news’ to search for news articles, searching within a specific domain is a common practice. Sandhan is one such project which offers domain specific search for Tourism and Health domains across 10 different Indian Languages. Much of the accuracy of a web page classification algorithm depends on the data it gets trained on. The motivation behind this paper is to provide a proper set of guidelines to collect and store this data in an efficient and an error free way. The major contribution of this paper would be a Hindi web page collection manually classified into Tourism, Health and Miscellaneous.

Abstract

Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a twostep classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog. The systems use features derived from the Partof-speech tags, the tweet content and uses external resources such as query logs and n-gram dictionaries from previously labeled data. This work aims at providing a thorough insight into the problem and analyzing various features to assess which features contribute the most towards identifying the tweets that can be targeted for advertisements.