UBVRI CCD photometry of the OB associations Bochum 1 and Bochum 6

We report the first deep $UBVRI$ CCD photometry of 2460 stars in the field of two poorly studied OB associations Bochum 1 and Bochum 6. We selected 15 and 14 probable members in Bochum 1 and Bochum 6 respectively using photometric criteria and proper motion data of Tycho 2. Our analysis indicates variable reddening having mean value of $E(B-V)=$ 0.47$\pm$0.10 and 0.71$\pm$0.13 mag for Bochum 1 and Bochum 6 respectively. Using the zero-age main-sequence fitting method, we derive a distance of 2.8$\pm$0.4 and 2.5$\pm$0.4 Kpc for Bochum 1 and Bochum 6 respectively. We obtain an age of 10$\pm$5 Myrs for both the associations from isochrone fitting. In both associations high and low mass stars have probably formed together. Within the observational uncertainties, mass spectrum of the both associations appears to be similar to the Salpeter's one.Comment: 14 pages, 7 figures, 6 tables. Accepted for Bull. Astr. Soc. Indi

Non-uniform extinction in young open star clusters

The extinction law and the variation of colour excess with position, luminosity as well as spectral class in young open star clusters NGC 663, NGC869, NGC 884, NGC 1502, NGC 1893, NGC 2244, NGC 2264, NGC 6611, Tr 14, Tr 15,Tr 16, Coll 228, Tr 37 and Be 86 have been studied. The difference in the minimum and maximum values of E(B-V) of cluster members has been considered as a measure of the presence of non-uniform gas and dust inside the clusters. Its value ranges from 0.22 to 1.03 mag in clusters under study, which indicates that non-uniform extinction is present in all the clusters. It has been noticed for the first time in NGC 1502 and Tr 37. It is also found that the differential colour excess in open clusters, which may be due to the presence of gas and dust, decreases systematically with the age of clusters indicating that matter is used either in star formation or blown away by hot stars or both. There is no uniformity in the variation of E(B-V) with either position or spectral class or luminosity.Comment: 11 pages, 8 figures, 4 tables; accepted for publication in MNRAS, typos adde

Effect of Irrigation and Potash Levels on Keeping Quality of Potato

Irrigation and fertilizer are the most dominating factors, in deciding the keeping quality of potato. It is, therefore, essential to formulate the efficient, reliable and economically viable irrigation management strategy with the use of potassium nutrient in order to produce better keeping quality. The investigation comprising four levels of irrigation (25, 30, 35 and 40 mm CPE (Cumulative pan evaporation) and four levels of potash (0, 100, 125 and 150 kg/ha) was carried out at Research Farm of the Department of Vegetable Science, CCS Haryana Agricultural University, (Haryana) Hisar, India during two years to find out the optimum level of irrigation and potash for obtaining higher yield of potatoes with better keeping quality at ambient room temperature. The potato variety used for the investigation was Kufri Bahar. The treatments were laid out in a split plot design with three replications. The increasing levels of irrigation and potash showed significant improvement in keeping quality parameters of potato. Likewise, the values for physiological loss in weight and decay loss of potato tubers (%) at 15, 30, 45 and 60 days after harvest were the lowest with irrigation level 40 mm CPE and application of potash @ 150 kg/ha. The two years results suggest that the irrigation level 40 mm CPE along with potash @ 150 kg/ha has shown the best treatment combination for the storage of potato at ambient room temperature under semiarid conditions of Hisar (Haryana)

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201