Electron Magnetic Resonance Studies of Nanosized Nd0.65Ca0.35 Mn1-xCrxO3 (x=0, 0.06, 0.1) Manganite

We report a systematic investigation of the temperature dependence of electron magnetic resonance (EMR) line width, intensity and resonance field for similar to 25 nm Nd0.65Ca0.35MnO3 (NCMO1), Nd0.65Ca0.35Mn0.94Cr0.06O3 (NCMO2) and Nd0.65Ca0.35Mn0.9Cr0.1O3 (NCMO3) nanoparticles prepared by sol-gel method. The EMR line widths for the three nano-samples differ significantly from one another below a temperature T (min) where the line width has a minimum. T (min) was found to be 130, 100 and 120 K for NCMO1, NCMO2 and NCMO3, respectively. Well above T (min) the line width values for the three samples are close to one another. The sharp upturn of EMR line width below T (min) is attributed to the formation of short range, ferromagnetically ordered clusters. Temperature dependence of EMR intensity shows a residual CO transition in NCMO1 and NCMO2 and a complete disappearance of it in NCMO3. The intensity undergoes significant increase below 120, 80 and 100 K for NCMO1, NCMO2 and NCMO3, respectively, indicating the onset of ferromagnetic transitions. The occurrence of ferromagnetic transition is further confirmed by magnetization hysteresis measurements. The decrease in T (C) in NCMO2 and NCMO3 compared to NCMO1 nanoparticles is understood to be due to the destruction of the double-exchange interaction by chromium doping. The resonance field decreases below the ferromagnetic onset temperatures for all the samples as expected. The combined effects of the reduction in size and of chromium doping in Mn site are discussed.

Translation Induction on Indian Language Corpora Using Translingual Themes from Other Languages

Identifying translations from comparable corpora is a well-known problem with several applications, e.g. dictionary creation in resource-scarce languages. Scarcity of high quality corpora, especially in Indian languages, makes this problem hard, e.g. state-of-the-art techniques achieve a mean reciprocal rank (MRR) of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. There exist comparable corpora in many Indian languages with other ``auxiliary'' languages. We observe that translations have many topically related words in common in the auxiliary language. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for translation induction. Extensive experiments on 35 comparable corpora using English and French as auxiliary languages show that this approach can yield dramatic improvements in performance (e.g. MRR improves by 124% to 0.419 for Telugu-Kannada). A user study on WikiTSu, a system for cross-lingual Wikipedia title suggestion that uses our approach, shows a 20% improvement in the quality of titles suggested.