Room Temperature-Processed TiO2 MIM Capacitors for DRAM Applications

We report the room temperature fabrication of Ta/TiO2/Ta metal-insulator-metal (MIM) capacitors (mainly, for DRAM applications). The fabricated devices show high capacitance density (similar to 15 fF/mu m(2)), and low leakage current density of 6.4 X 10(-8) A/cm(2) (27 degrees C) and 3.3 x 10(-6) A/cm(2) (125 degrees C) at -1 V. We analyze the electrical and material characteristics of the fabricated capacitors, and compare the device performance of these capacitors with other TiO2 and TiO2-based MIM capacitors reported in recent literature.

Stress Engineering using Si3N4 for stiction free release of SOI beams

We report on the effect of thin silicon nitride (Si3N4) induced tensile stress on the structural release of 200nm thick SOI beam, in the surface micro-machining process. A thin (20nm / 100nm) LPCVD grown Si3N4 is shown to significantly enhance the yield of released beam in wet release technique. This is especially prominent with increase in beam length, where the beams have higher tendency for stiction. We attribute this yield enhancement to the nitride induced tensile stress, as verified by buckling tendency and resonance frequency data obtained from optical profilometry and laser doppler vibrometry.

Surface Enhanced Raman Scattering on Anodized Alumina Templates for Bio-sensing Applications

SERS substrate was fabricated by depositing silver on anodized aluminum oxide (AAO) template. The thickness of the AA0 template was 200 nm with 40 nm circular pore and 15 nm spacing. SERS effect was observed on these metal coated structures due to electric field enhancement around the edge of the pores. Para-Nitrophenol (pnp) solution of 10(-6) M concentration was detected which refers to an enhancement factor of 10(4).

Gold Catalyzed Plasma Assisted Growth of Germanium Nanoneedles

A systematic study of Gold catalyzed growth of Ge nanoneedles by PECVD at low temperatures (<400 degrees C) is presented. Morphology, growth rate and aspect ratio of the needles are studied as a function of power, gas flow rate and chamber pressure. Nanoneedles were grown at pre-defined positions with catalytic particles obtained by e-Beam Lithography and lift off. This opens up the possibility of using Ge Nano needles in photovoltaic, nanoelectronics and nanosensor device applications.

Conductivity Studies of Silver-, Potassium-, and Magnesium-Doped Hydroxyapatite

We report the electrical transport properties of silver-, potassium-, and magnesium-doped hydroxyapatites (HAs). While Ag+ or K+ doping to HA enhances the conductivity, Mg+2 doping lowers the conductivity when compared with undoped HA. The mechanism behind the observed differences in ionic conductivity has been discussed using the analysis of high-temperature frequency-dependent conductivity data, Cole-Cole plots of impedance data as well as on the basis of the frequency dependence of the imaginary part (M) of the complex electric modulus. The f(max) of modulus M decreased in silver- and potassium-doped samples in comparison with the undoped HA.

Combined toxicity of two crystalline phases (anatase and rutile) of Titania nanoparticles towards freshwater microalgae: Chlorella sp

In view of the increasing usage of anatase and rutile crystalline phases of titania NPs in the consumer products, their entry into the aquatic environment may pose a serious risk to the ecosystem. In the present study, the possible toxic impact of anatase and rutile nanoparticles (individually and in binary mixture) was investigated using freshwater microalgae, Chlorella sp. at low exposure concentrations (0.25, 0.5 and 1 mg/L) in freshwater medium under UV irradiation. Reduction of cell viability as well as a reduction in chlorophyll content were observed due to the presence of NPs. An antagonistic effect was noted at certain concentrations of binary mixture such as (0.25, 0.25), (0.25, 0.5), and (0.5, 0.5) mg/L, and an additive effect for the other combinations, (0.25, 1), (0.5, 0.25), (0.5, 1), (1, 0.25), (1, 0.5), and (1, 1) mg/L. The hydrodynamic size analyses in the test medium revealed that rutile NPs were more stable in lake water than the anatase and binary mixtures at 6 h, the sizes of anatase (1 mg/L), rutile NPs (1 mg/L), and binary mixture (1, 1 mg/L) were 948.83 +/- 35.01 nm, 555.74 +/- 19.93 nm, and 1620.24 +/- 237.87 nm, respectively]. The generation of oxidative stress was found to be strongly dependent on the crystallinity of the nanoparticles. The transmission electron microscopic images revealed damages in the nucleus and cell membrane of algal cells due to the interaction of anatase NPs, whereas rutile NPs were found to cause chloroplast and internal organelle damages. Mis-shaped chloroplasts, lack of nucleus, and starch-pyrenoid complex were noted in binary-treated cells. The findings from the current study may facilitate the environmental risk assessment of titania NPs in an aquatic ecosystem. (C) 2015 Elsevier B.V. All rights reserved.

Prediction of Queue Waiting Times for Metascheduling on Parallel Batch Systems

Prediction of queue waiting times of jobs submitted to production parallel batch systems is important to provide overall estimates to users and can also help meta-schedulers make scheduling decisions. In this work, we have developed a framework for predicting ranges of queue waiting times for jobs by employing multi-class classification of similar jobs in history. Our hierarchical prediction strategy first predicts the point wait time of a job using dynamic k-Nearest Neighbor (kNN) method. It then performs a multi-class classification using Support Vector Machines (SVMs) among all the classes of the jobs. The probabilities given by the SVM for the class predicted using k-NN and its neighboring classes are used to provide a set of ranges of predicted wait times with probabilities. We have used these predictions and probabilities in a meta-scheduling strategy that distributes jobs to different queues/sites in a multi-queue/grid environment for minimizing wait times of the jobs. Experiments with different production supercomputer job traces show that our prediction strategies can give correct predictions for about 77-87% of the jobs, and also result in about 12% improved accuracy when compared to the next best existing method. Experiments with our meta-scheduling strategy using different production and synthetic job traces for various system sizes, partitioning schemes and different workloads, show that the meta-scheduling strategy gives much improved performance when compared to existing scheduling policies by reducing the overall average queue waiting times of the jobs by about 47%.

Simultaneous nonvanishing of Dirichlet L-functions and twists of Hecke-Maass L-functions

We prove that given a Hecke-Maass form f for SL(2, Z) and a sufficiently large prime q, there exists a primitive Dirichlet character chi of conductor q such that the L-values L(1/2, f circle times chi) and L(1/2, chi) do not vanish.

Parameterized Algorithms and Kernels for 3-Hitting Set with Parity Constraints

The 3-Hitting Set problem involves a family of subsets F of size at most three over an universe U. The goal is to find a subset of U of the smallest possible size that intersects every set in F. The version of the problem with parity constraints asks for a subset S of size at most k that, in addition to being a hitting set, also satisfies certain parity constraints on the sizes of the intersections of S with each set in the family F. In particular, an odd (even) set is a hitting set that hits every set at either one or three (two) elements, and a perfect code is a hitting set that intersects every set at exactly one element. These questions are of fundamental interest in many contexts for general set systems. Just as for Hitting Set, we find these questions to be interesting for the case of families consisting of sets of size at most three. In this work, we initiate an algorithmic study of these problems in this special case, focusing on a parameterized analysis. We show, for each problem, efficient fixed-parameter tractable algorithms using search trees that are tailor-made to the constraints in question, and also polynomial kernels using sunflower-like arguments in a manner that accounts for equivalence under the additional parity constraints.

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.

Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning

Despite significant advances in recent years, structure-from-motion (SfM) pipelines suffer from two important drawbacks. Apart from requiring significant computational power to solve the large-scale computations involved, such pipelines sometimes fail to correctly reconstruct when the accumulated error in incremental reconstruction is large or when the number of 3D to 2D correspondences are insufficient. In this paper we present a novel approach to mitigate the above-mentioned drawbacks. Using an image match graph based on matching features we partition the image data set into smaller sets or components which are reconstructed independently. Following such reconstructions we utilise the available epipolar relationships that connect images across components to correctly align the individual reconstructions in a global frame of reference. This results in both a significant speed up of at least one order of magnitude and also mitigates the problems of reconstruction failures with a marginal loss in accuracy. The effectiveness of our approach is demonstrated on some large-scale real world data sets.

Activity Recognition in Egocentric Life-Logging Videos

With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/similar to 1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.