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A reverse dictionary takes a target word's description as input and returns the words that fit the description. Reverse Dictionaries are useful for new language learners, anomia patients, and for solving common tip-of-the-tongue problems (lethologica). Currently, there does not exist any Reverse Dictionary provider with support for any Indian Language. We present a novel open-source cross-lingual reverse dictionary system with support for Indian languages. In this paper, we propose a transformer-based deep learning approach to tackle the limitations faced by the existing systems using the mT5 model. This architecture uses the Translation Language Modeling (TLM) technique, rather than the conventional BERT's Masked Language Modeling (MLM) technique.

Coal mines are globally an important source of methane and also one of the largest point sources of methane. We present a high-resolution 0.1deg x 0.1deg bottom-up gridded emission inventory for methane emissions from coal mines in India and Australia, which are among the top five coal-producing countries in 2018. The aim is to reduce the uncertainty in local coal mine methane emissions and to improve the spatial localization to support monitoring and mitigation of these emissions. For India, we improve the spatial allocation of the emissions by identifying the exact location of surface and underground coal mines and we use a tier-2 Intergovernmental Panel on Climate Change (IPCC) methodology to estimate the emissions from each coal mine using country-specific emission factors. For Australia, we estimate the emission for each coal mine by distributing the state-level reported total emissions using proxies of coal production and the coal basin-specific gas content profile of underground mines. Comparison of our total coal mine methane emission from India with existing global inventories showed our estimates are about a factor 3 lower, but well within the range of the national Indian estimate reported to the United Nations framework convention on climate change (UNFCCC). For both countries, the new spatial distribution of the emissions shows a large difference from the global inventories. Our improved emissions dataset will be useful for air quality or climate modeling and while assessing the satellite methane observations.

Two years of satellite observations were used to quantify methane emission from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570+/-98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production, while emitting 55+/-10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.