Adversarially Tuned Scene Generation

Generalization performance of trained computer vision systems that use computer graphics (CG) generated data is not yet effective due to the concept of 'domain-shift' between virtual and real data. Although simulated data augmented with a few real world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, and intrinsic images etc.). In order to address this problem in an unsupervised manner, our work combines recent advances in CG (which aims to generate stochastic scene layouts coupled with large collections of 3D object models) and generative adversarial training (which aims train generative models by measuring discrepancy between generated and real data in terms of their separability in the space of a deep discriminatively-trained classifier). Our method uses iterative estimation of the posterior density of prior distributions for a generative graphical model. This is done within a rejection sampling framework. Initially, we assume uniform distributions as priors on the parameters of a scene described by a generative graphical model. As iterations proceed the prior distributions get updated to distributions that are closer to the (unknown) distributions of target data. We demonstrate the utility of adversarially tuned scene generation on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene semantic labeling with a deep convolutional net (DeepLab). We realized performance improvements by 2.28 and 3.14 points (using the IoU metric) between the DeepLab models trained on simulated sets prepared from the scene generation models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201

Impact of different tillage methods on growth, development and productivity of maize (Zea mays)-wheat (Tritcum aestivum) cropping system

An experiment was conducted on a silty clay loam soil of Palampur during 2009–2011, to study the effect of different tillage methods in maize (Zea mays L.) wheat {Triticum aestivum (L.) emend. Fiori & Paol.} cropping system. Results revealed that in maize crop, tillage methods in kharif season resulted in significantly highest emergence count (27.1 plant/m2) under manual seed drill. While, multi-crop planter recorded in significantly taller plants (55.4 cm) at 30 DAS; higher dry matter accumulation 81.0, 990.0 and 4184.4 g/m2 at 30, 60 and 90 DAS, respectively and CGR (30.3 g/day/m2) at 30-60 DAS. Tillage methods in rabi season resulted in higher emergence count (17.6 plant/m2) under zero tillage. This treatment also recorded advanced emergence by 1.2 to 1.5 days. In wheat crop, tillage methods in kharif season resulted in significantly highest emergence count (307.6 plant/m2), taller plants (13.1 cm) at 30 DAS, dry matter accumulation (625.3 g/m2) at 120 DAS and CGR (14.4 g/day/m2) at 90-120 DAS under conventional tillage. While, tillage methods in rabi season resulted in significantly highest emergence count (369.5 plants/m2), tallest plants (17.7, 92.6 and 101.0 cm at 60, 120 and at harvest, respectively) with multi-crop planter. While, zero tillage recorded significantly higher CGR (15.8 g/day/m2) and RGR (0.027 g/g/day) during 120-harvest stage. Zero tillage produced statistically at par crop yield and rainwater-use efficiency of both crops with other tillage treatments. Hence, zero tillage can be as good as other intensive tillage system besides lower input cost and environmental security

Electric Field Effect in Diluted Magnetic Insulator Anatase Co:TiO2

An external electric field induced reversible modulation of room temperature magnetic moment is achieved in an epitaxial and insulating thin film of dilutely cobalt-doped anatase TiO2. This first demonstration of electric field effect in any oxide based diluted ferromagnet is realized in a high quality epitaxial heterostructure of PbZr0.2Ti0.8O3/Co:TiO2/SrRuO3 grown on (001) LaAlO3. The observed effect, which is about 15% in strength in a given heterostructure, can be modulated over several cycles. Possible mechanisms for electric field induced modulation of insulating ferromagnetism are discussed.Comment: 14 pages, 4 figure

Studies on the Production of Protease by Bacillus Species

The different bacterial isolates such as Bacillus subtilis, Bacillus coagulans, Bacillus firmus and mixed culture were screened for protease production.  A basal medium containing peptone, beef extract and NaCl was used for this biomass cultivation. Among the different bacterial isolates screened, the maximum protease production was found in B. coagulans and minimum in B. subtilis in both quantitative and semi quantitative assay. The effect of various environmental conditions like pH, temperature, rpm and inoculum size on alkaline protease production was examined.  The optimum condition for protease production upon inoculation of 1ml of overnight grown culture was found to be pH 9.2, 37º C, 175 rpm and 1% of inoculum for a time period of 24 h.  One way analysis of variance showed significant differences (P < 0.05) in protease production between different species

On the polar decomposition of right linear operators in quaternionic Hilbert spaces

In this article we prove the existence of the polar decomposition for densely defined closed right linear operators in quaternionic Hilbert spaces: If $T$ is a densely defined closed right linear operator in a quaternionic Hilbert space $H$, then there exists a partial isometry $U_{0}$ such that $T = U_{0}|T|$. In fact $U_{0}$ is unique if $N(U_{0}) = N(T)$. In particular, if $H$ is separable and $U$ is a partial isometry with $T = U|T|$, then we prove that $U = U_{0}$ if and only if either $N(T) = \{0\}$ or $R(T)^{\bot} = \{0\}$.Comment: 17 page

Artificial neural network model for arrival time computation in gate level circuits

Advances in the VLSI process technology lead to variations in the process parameters. These process variations severely affect the delay computation of a digital circuit. Under such variations, the various delays, i.e. net delay, gate delay, etc., are no longer deterministic. They are random in nature and are assumed to be probabilistic. They keep changing, based on factors such as process, voltage, temperature, and a few others. This calls for efficient tools to perform timing checks on a design. This work presents a technique to compute the arrival time of a digital circuit. The arrival time (AT) is computed using two different timing engines, namely, static timing analysis (STA) and statistical static timing analysis (SSTA). This work also aims to eliminate number of false paths. It uses a fast and efficient filtering method by utilizing ATPG stuck-at faults and path delay faults. ISCAS-89 benchmark circuits are used for implementation. The results obtained using the probabilistic approach are more accurate than the conventional STA. It has been verified with an Artificial Neural Network (ANN) model. The arrival time calculated using SSTA shows 7% improvement over that of STA. The absolute error is reduced twofold in the case of the ANN model for SSTA