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Nitrogen hydrides such as NH3 and N2H+ are widely used by Galactic observers to trace the cold dense regions of the interstellar medium. In external galaxies, because of limited sensitivity, HCN has become the most common tracer of dense gas over large parts of galaxies. We provide the first systematic measurements of N2H+ (1-0) across different environments of an external spiral galaxy, NGC 6946. We find a strong correlation (r > 0.98, p < 0.01) between the HCN (1-0) and N2H+ (1-0) intensities across the inner ∼8 kpc of the galaxy, at kiloparsec scales. This correlation is equally strong between the ratios N2H+ (1-0)/CO (1-0) and HCN (1-0)/CO (1-0), tracers of dense gas fractions (fdense). We measure an average intensity ratio of N2H+ (1-0)/HCN (1-0) = 0.15 ± 0.02 over our set of five IRAM-30m pointings. These trends are further supported by existing measurements for Galactic and extragalactic sources. This narrow distribution in the average ratio suggests that the observed systematic trends found in kiloparsec-scale extragalactic studies of fdense and the efficiency of dense gas (SFEdense) would not change if we employed N2H+ (1-0) as a more direct tracer of dense gas. At kiloparsec scales our results indicate that the HCN (1-0) emission can be used to predict the expected N2H+ (1-0) over those regions. Our results suggest that, even if HCN (1-0) and N2H+ (1-0) trace different density regimes within molecular clouds, subcloud differences average out at kiloparsec scales, yielding the two tracers proportional to each other. 

Galaxy evolution is regulated by the continuous cycle of gas accretion, consumption and feedback. Crucial in this cycle is the availability of neutral atomic (HI) and molecular hydrogen. Our current inventory of HI, however, is very limited beyond the local Universe (z > 0.25), resulting in an incomplete picture. ORCHIDSS is designed to address this critical challenge, using the powerful combination of 4MOST spectroscopy and sensitive radio observations from the MeerKAT deep extragalactic surveys to trace the evolution of neutral gas and its lifecycle within galaxies across the bulk of cosmic history. 

Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities.